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Merge pull request #417 from mipt-npm/dev

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0.3.0-dev-15
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Alexander Nozik 2021-10-13 14:56:53 +03:00 committed by GitHub
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20
.github/workflows/build.yml vendored
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@ -1,6 +1,9 @@
name: Gradle build
on: [ push ]
on:
push:
branches: [ dev, master ]
pull_request:
jobs:
build:
@ -8,23 +11,22 @@ jobs:
matrix:
os: [ macOS-latest, windows-latest ]
runs-on: ${{matrix.os}}
timeout-minutes: 30
timeout-minutes: 40
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
graalvm: 21.2.0
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
path: |
~/.gradle/caches
~/.gradle/wrapper
key: ${{ runner.os }}-gradle-${{ hashFiles('*.gradle.kts') }}
restore-keys: |
${{ runner.os }}-gradle-
@ -35,5 +37,7 @@ jobs:
key: ${{ runner.os }}-gradle-${{ hashFiles('*.gradle.kts') }}
restore-keys: |
${{ runner.os }}-gradle-
- name: Gradle Wrapper Validation
uses: gradle/wrapper-validation-action@v1.0.4
- name: Build
run: ./gradlew build --no-daemon --stacktrace
run: ./gradlew build --build-cache --no-daemon --stacktrace

26
.github/workflows/pages.yml vendored
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@ -2,23 +2,27 @@ name: Dokka publication
on:
push:
branches:
- master
branches: [ master ]
jobs:
build:
runs-on: ubuntu-20.04
timeout-minutes: 40
steps:
- name: Checkout the repo
uses: actions/checkout@v2
- name: Set up JDK 11
uses: actions/setup-java@v1
- uses: actions/checkout@v2
- uses: DeLaGuardo/setup-graalvm@4.0
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
graalvm: 21.2.0
java: java11
arch: amd64
- uses: actions/cache@v2
with:
path: ~/.gradle/caches
key: ${{ runner.os }}-gradle-${{ hashFiles('*.gradle.kts') }}
restore-keys: |
${{ runner.os }}-gradle-
- run: ./gradlew dokkaHtmlMultiModule --build-cache --no-daemon --no-parallel --stacktrace
- uses: JamesIves/github-pages-deploy-action@4.1.0
with:
branch: gh-pages
folder: build/dokka/htmlMultiModule

34
.github/workflows/publish.yml vendored
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@ -3,8 +3,7 @@ name: Gradle publish
on:
workflow_dispatch:
release:
types:
- created
types: [ created ]
jobs:
publish:
@ -20,16 +19,15 @@ jobs:
- name: Set up JDK 11
uses: DeLaGuardo/setup-graalvm@4.0
with:
graalvm: 21.1.0
graalvm: 21.2.0
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
path: |
~/.gradle/caches
~/.gradle/wrapper
key: ${{ runner.os }}-gradle-${{ hashFiles('*.gradle.kts') }}
restore-keys: |
${{ runner.os }}-gradle-
@ -40,22 +38,18 @@ jobs:
key: ${{ runner.os }}-gradle-${{ hashFiles('*.gradle.kts') }}
restore-keys: |
${{ runner.os }}-gradle-
- name: Gradle Wrapper Validation
uses: gradle/wrapper-validation-action@v1.0.4
- name: Publish Windows Artifacts
if: matrix.os == 'windows-latest'
shell: cmd
run: >
./gradlew release --no-daemon
-Ppublishing.enabled=true
-Ppublishing.github.user=${{ secrets.PUBLISHING_GITHUB_USER }}
-Ppublishing.github.token=${{ secrets.PUBLISHING_GITHUB_TOKEN }}
-Ppublishing.space.user=${{ secrets.PUBLISHING_SPACE_USER }}
-Ppublishing.space.token=${{ secrets.PUBLISHING_SPACE_TOKEN }}
./gradlew release --no-daemon --build-cache -Ppublishing.enabled=true
-Ppublishing.space.user=${{ secrets.SPACE_APP_ID }}
-Ppublishing.space.token=${{ secrets.SPACE_APP_SECRET }}
- name: Publish Mac Artifacts
if: matrix.os == 'macOS-latest'
run: >
./gradlew release --no-daemon
-Ppublishing.enabled=true
-Ppublishing.platform=macosX64
-Ppublishing.github.user=${{ secrets.PUBLISHING_GITHUB_USER }}
-Ppublishing.github.token=${{ secrets.PUBLISHING_GITHUB_TOKEN }}
-Ppublishing.space.user=${{ secrets.PUBLISHING_SPACE_USER }}
-Ppublishing.space.token=${{ secrets.PUBLISHING_SPACE_TOKEN }}
./gradlew release --no-daemon --build-cache -Ppublishing.enabled=true -Ppublishing.platform=macosX64
-Ppublishing.space.user=${{ secrets.SPACE_APP_ID }}
-Ppublishing.space.token=${{ secrets.SPACE_APP_SECRET }}

2
.idea/copyright/kmath.xml
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@ -1,6 +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="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 file." />
<option name="myName" value="kmath" />
</copyright>
</component>

38
CHANGELOG.md
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@ -2,32 +2,49 @@
## [Unreleased]
### Added
- ScaleOperations interface
- Field extends ScaleOperations
- `ScaleOperations` interface
- `Field` extends `ScaleOperations`
- Basic integration API
- Basic MPP distributions and samplers
- bindSymbolOrNull
- `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
- Unified architecture for Integration and Optimization using features.
- `BigInt` operation performance improvement and fixes by @zhelenskiy (#328)
- Integration between `MST` and Symja `IExpr`
- Complex power
### 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
- NDStructure and NDAlgebra to StructureND and AlgebraND respectively
- Real -> Double
- 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
- Redesign `MST`. Remove `MstExpression`.
- Move `MST` to core
- Separated benchmarks and examples
- Rewritten EJML module without ejml-simple
- 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`
- `FeatureSet` now accepts only `Feature`. It is possible to override keys and use interfaces.
- Use `Symbol` factory function instead of `StringSymbol`
- New discoverability pattern: `<Type>.algebra.<nd/etc>`
- Adjusted commons-math API for linear solvers to match conventions.
### Deprecated
- Specialized `DoubleBufferAlgebra`
### Removed
- Nearest in Domain. To be implemented in geometry package.
@ -35,10 +52,13 @@
- `contentEquals` from Buffer. It moved to the companion.
- MSTExpression
- Expression algebra builders
- Comples and Quaternion no longer are elements.
- Complex and Quaternion no longer are elements.
- Second generic from DifferentiableExpression
- Algebra elements are completely removed. Use algebra contexts instead.
### Fixed
- Ring inherits RingOperations, not GroupOperations
- Univariate histogram filling
### Security

201
LICENSE
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@ -1,201 +0,0 @@
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107
README.md
View File

@ -2,14 +2,14 @@
[![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)
[![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/maven-metadata/v?label=Space&metadataUrl=https%3A%2F%2Fmaven.pkg.jetbrains.space%2Fmipt-npm%2Fp%2Fsci%2Fmaven%2Fkscience%2Fkmath%2Fkmath-core%2Fmaven-metadata.xml)](https://maven.pkg.jetbrains.space/mipt-npm/p/sci/maven/space/kscience/)
[![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
Could be pronounced as `key-math`. The **K**otlin **Math**ematics library was initially intended as a Kotlin-based analog to
Python's NumPy library. Later we found that kotlin is much more flexible language and allows superior architecture
designs. In contrast to `numpy` and `scipy` it is modular and has a lightweight core. The `numpy`-like experience could
be achieved with [kmath-for-real](/kmath-for-real) extension module.
Could be pronounced as `key-math`. The **K**otlin **Math**ematics library was initially intended as a Kotlin-based
analog to Python's NumPy library. Later we found that kotlin is much more flexible language and allows superior
architecture designs. In contrast to `numpy` and `scipy` it is modular and has a lightweight core. The `numpy`-like
experience could be achieved with [kmath-for-real](/kmath-for-real) extension module.
[Documentation site (**WIP**)](https://mipt-npm.github.io/kmath/)
@ -21,26 +21,33 @@ be achieved with [kmath-for-real](/kmath-for-real) extension module.
# Goal
* Provide a flexible and powerful API to work with mathematics abstractions in Kotlin-multiplatform (JVM, JS and Native).
* Provide a flexible and powerful API to work with mathematics abstractions in Kotlin-multiplatform (JVM, JS and Native)
.
* Provide basic multiplatform implementations for those abstractions (without significant performance optimization).
* Provide bindings and wrappers with those abstractions for popular optimized platform libraries.
## Non-goals
* Be like NumPy. It was the idea at the beginning, but we decided that we can do better in terms of API.
* Be like NumPy. It was the idea at the beginning, but we decided that we can do better in API.
* Provide the best performance out of the box. We have specialized libraries for that. Need only API wrappers for them.
* Cover all cases as immediately and in one bundle. We will modularize everything and add new features gradually.
* Provide specialized behavior in the core. API is made generic on purpose, so one needs to specialize for types, like
for `Double` in the core. For that we will have specialization modules like `kmath-for-real`, which will give better
experience for those, who want to work with specific types.
* Provide specialized behavior in the core. API is made generic on purpose, so one needs to specialize for types, like
for `Double` in the core. For that we will have specialization modules like `kmath-for-real`, which will give better
experience for those, who want to work with specific types.
## Features and stability
KMath is a modular library. Different modules provide different features with different API stability guarantees. All core modules are released with the same version, but with different API change policy. The features are described in module definitions below. The module stability could have following levels:
KMath is a modular library. Different modules provide different features with different API stability guarantees. All
core modules are released with the same version, but with different API change policy. The features are described in
module definitions below. The module stability could have the following levels:
* **PROTOTYPE**. On this level there are no compatibility guarantees. All methods and classes form those modules could break any moment. You can still use it, but be sure to fix the specific version.
* **EXPERIMENTAL**. The general API is decided, but some changes could be made. Volatile API is marked with `@UnstableKmathAPI` or other stability warning annotations.
* **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.
* **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 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)-->
@ -132,7 +139,7 @@ KMath is a modular library. Different modules provide different features with di
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/space/kscience/kmath/domains) : Domains
> - [autodif](kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/SimpleAutoDiff.kt) : Automatic differentiation
> - [autodiff](kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/SimpleAutoDiff.kt) : Automatic differentiation
<hr/>
@ -161,7 +168,7 @@ performance calculations to code generation.
<hr/>
* ### [kmath-for-real](kmath-for-real)
> Extension module that should be used to achieve numpy-like behavior.
> Extension module that should be used to achieve numpy-like behavior.
All operations are specialized to work with `Double` numbers without declaring algebraic contexts.
One can still use generic algebras though.
>
@ -175,7 +182,7 @@ One can still use generic algebras though.
<hr/>
* ### [kmath-functions](kmath-functions)
> Functions, integration and interpolation
>
>
> **Maturity**: EXPERIMENTAL
>
@ -200,6 +207,16 @@ One can still use generic algebras though.
> **Maturity**: PROTOTYPE
<hr/>
* ### [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)
>
>
@ -209,17 +226,22 @@ One can still use generic algebras though.
* ### [kmath-kotlingrad](kmath-kotlingrad)
>
>
> **Maturity**: PROTOTYPE
> **Maturity**: EXPERIMENTAL
>
> **Features:**
> - [differentiable-mst-expression](kmath-kotlingrad/src/main/kotlin/space/kscience/kmath/kotlingrad/KotlingradExpression.kt) : MST based DifferentiableExpression.
> - [scalars-adapters](kmath-kotlingrad/src/main/kotlin/space/kscience/kmath/kotlingrad/scalarsAdapters.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
>
@ -236,6 +258,12 @@ One can still use generic algebras though.
> **Maturity**: EXPERIMENTAL
<hr/>
* ### [kmath-symja](kmath-symja)
>
>
> **Maturity**: PROTOTYPE
<hr/>
* ### [kmath-tensors](kmath-tensors)
>
>
@ -243,7 +271,7 @@ One can still use generic algebras though.
>
> **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.
> - [tensor algebra with broadcasting](kmath-tensors/src/commonMain/kotlin/space/kscience/kmath/tensors/core/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/>
@ -257,30 +285,33 @@ One can still use generic algebras though.
## Multi-platform support
KMath is developed as a multi-platform library, which means that most of the interfaces are declared in the
[common source sets](/kmath-core/src/commonMain) and implemented there wherever it is possible. In some cases, features
are delegated to platform-specific implementations even if they could be provided in the common module for performance
reasons. Currently, the Kotlin/JVM is the primary platform, however Kotlin/Native and Kotlin/JS contributions and
KMath is developed as a multi-platform library, which means that most of the interfaces are declared in the
[common source sets](/kmath-core/src/commonMain) and implemented there wherever it is possible. In some cases, features
are delegated to platform-specific implementations even if they could be provided in the common module for performance
reasons. Currently, the Kotlin/JVM is the primary platform, however Kotlin/Native and Kotlin/JS contributions and
feedback are also welcome.
## Performance
Calculation performance is one of major goals of KMath in the future, but in some cases it is impossible to achieve
both performance and flexibility.
Calculation performance is one of major goals of KMath in the future, but in some cases it is impossible to achieve both
performance and flexibility.
We expect to focus on creating convenient universal API first and then work on increasing performance for specific
cases. We expect the worst KMath benchmarks will perform better than native Python, but worse than optimized
native/SciPy (mostly due to boxing operations on primitive numbers). The best performance of optimized parts could be
We expect to focus on creating convenient universal API first and then work on increasing performance for specific
cases. We expect the worst KMath benchmarks will perform better than native Python, but worse than optimized
native/SciPy (mostly due to boxing operations on primitive numbers). The best performance of optimized parts could be
better than SciPy.
## 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.
KMath currently relies on JDK 11 for compilation and execution of Kotlin-JVM part. We recommend to use GraalVM-CE 11 for
execution 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
[Kotlin Multiplatform](https://kotlinlang.org/docs/reference/multiplatform.html) for more details). The repository could be reached through [repo.kotlin.link](https://repo.kotlin.link) proxy:
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
[Kotlin Multiplatform](https://kotlinlang.org/docs/reference/multiplatform.html) for more details). The repository could
be reached through [repo.kotlin.link](https://repo.kotlin.link) proxy:
```kotlin
repositories {
@ -288,8 +319,8 @@ repositories {
}
dependencies {
api("space.kscience:kmath-core:0.3.0-dev-8")
// api("space.kscience:kmath-core-jvm:0.3.0-dev-8") 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
}
```
@ -297,7 +328,7 @@ Gradle `6.0+` is required for multiplatform artifacts.
## Contributing
The project requires a lot of additional work. The most important thing we need is a feedback about what features are
required the most. Feel free to create feature requests. We are also welcome to code contributions,
especially in issues marked with
The project requires a lot of additional work. The most important thing we need is a feedback about what features are
required the most. Feel free to create feature requests. We are also welcome to code contributions, especially in issues
marked with
[waiting for a hero](https://github.com/mipt-npm/kmath/labels/waiting%20for%20a%20hero) label.

34
benchmarks/build.gradle.kts
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@ -1,3 +1,7 @@
@file:Suppress("UNUSED_VARIABLE")
import space.kscience.kmath.benchmarks.addBenchmarkProperties
plugins {
kotlin("multiplatform")
kotlin("plugin.allopen")
@ -12,6 +16,7 @@ repositories {
maven("https://repo.kotlin.link")
maven("https://clojars.org/repo")
maven("https://jitpack.io")
maven("http://logicrunch.research.it.uu.se/maven") {
isAllowInsecureProtocol = true
}
@ -30,7 +35,9 @@ kotlin {
implementation(project(":kmath-stat"))
implementation(project(":kmath-dimensions"))
implementation(project(":kmath-for-real"))
implementation("org.jetbrains.kotlinx:kotlinx-benchmark-runtime:0.3.0")
implementation(project(":kmath-jafama"))
implementation(project(":kmath-tensors"))
implementation("org.jetbrains.kotlinx:kotlinx-benchmark-runtime:0.3.1")
}
}
@ -41,8 +48,7 @@ kotlin {
implementation(project(":kmath-nd4j"))
implementation(project(":kmath-kotlingrad"))
implementation(project(":kmath-viktor"))
implementation("org.nd4j:nd4j-native:1.0.0-beta7")
implementation("org.nd4j:nd4j-native:1.0.0-M1")
// uncomment if your system supports AVX2
// val os = System.getProperty("os.name")
//
@ -76,6 +82,11 @@ benchmark {
include("BufferBenchmark")
}
configurations.register("nd") {
commonConfiguration()
include("NDFieldBenchmark")
}
configurations.register("dot") {
commonConfiguration()
include("DotBenchmark")
@ -95,6 +106,21 @@ benchmark {
commonConfiguration()
include("BigIntBenchmark")
}
configurations.register("jafamaDouble") {
commonConfiguration()
include("JafamaBenchmark")
}
configurations.register("viktor") {
commonConfiguration()
include("ViktorBenchmark")
}
configurations.register("viktorLog") {
commonConfiguration()
include("ViktorLogBenchmark")
}
}
// Fix kotlinx-benchmarks bug
@ -124,3 +150,5 @@ tasks.withType<org.jetbrains.kotlin.gradle.tasks.KotlinCompile> {
readme {
maturity = ru.mipt.npm.gradle.Maturity.EXPERIMENTAL
}
addBenchmarkProperties()

2
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ArrayBenchmark.kt
View File

@ -1,6 +1,6 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.benchmarks

76
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/BigIntBenchmark.kt
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@ -1,6 +1,6 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.benchmarks
@ -10,22 +10,36 @@ 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.operations.BigInt
import space.kscience.kmath.misc.UnstableKMathAPI
import space.kscience.kmath.operations.BigIntField
import space.kscience.kmath.operations.JBigIntegerField
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.operations.parseBigInteger
import java.math.BigInteger
private fun BigInt.pow(power: Int): BigInt = modPow(BigIntField.number(power), BigInt.ZERO)
@UnstableKMathAPI
@State(Scope.Benchmark)
internal class BigIntBenchmark {
val kmSmallNumber = BigIntField.number(100)
val jvmSmallNumber = JBigIntegerField.number(100)
val kmNumber = BigIntField.number(Int.MAX_VALUE)
val jvmNumber = JBigIntegerField.number(Int.MAX_VALUE)
val largeKmNumber = BigIntField { number(11).pow(100_000) }
val largeJvmNumber = JBigIntegerField { number(11).pow(100_000) }
val kmLargeNumber = BigIntField { number(11).pow(100_000U) }
val jvmLargeNumber: BigInteger = JBigIntegerField { number(11).pow(100_000) }
val bigExponent = 50_000
@Benchmark
fun kmSmallAdd(blackhole: Blackhole) = BigIntField {
blackhole.consume(kmSmallNumber + kmSmallNumber + kmSmallNumber)
}
@Benchmark
fun jvmSmallAdd(blackhole: Blackhole) = JBigIntegerField {
blackhole.consume(jvmSmallNumber + jvmSmallNumber + jvmSmallNumber)
}
@Benchmark
fun kmAdd(blackhole: Blackhole) = BigIntField {
blackhole.consume(kmNumber + kmNumber + kmNumber)
@ -36,6 +50,16 @@ internal class BigIntBenchmark {
blackhole.consume(jvmNumber + jvmNumber + jvmNumber)
}
@Benchmark
fun kmAddLarge(blackhole: Blackhole) = BigIntField {
blackhole.consume(kmLargeNumber + kmLargeNumber + kmLargeNumber)
}
@Benchmark
fun jvmAddLarge(blackhole: Blackhole) = JBigIntegerField {
blackhole.consume(jvmLargeNumber + jvmLargeNumber + jvmLargeNumber)
}
@Benchmark
fun kmMultiply(blackhole: Blackhole) = BigIntField {
blackhole.consume(kmNumber * kmNumber * kmNumber)
@ -43,7 +67,7 @@ internal class BigIntBenchmark {
@Benchmark
fun kmMultiplyLarge(blackhole: Blackhole) = BigIntField {
blackhole.consume(largeKmNumber*largeKmNumber)
blackhole.consume(kmLargeNumber*kmLargeNumber)
}
@Benchmark
@ -53,16 +77,36 @@ internal class BigIntBenchmark {
@Benchmark
fun jvmMultiplyLarge(blackhole: Blackhole) = JBigIntegerField {
blackhole.consume(largeJvmNumber*largeJvmNumber)
blackhole.consume(jvmLargeNumber*jvmLargeNumber)
}
// @Benchmark
// fun kmPower(blackhole: Blackhole) = BigIntField {
// blackhole.consume(kmNumber.pow(bigExponent))
// }
//
// @Benchmark
// fun jvmPower(blackhole: Blackhole) = JBigIntegerField {
// blackhole.consume(jvmNumber.pow(bigExponent))
// }
@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))
}
}

2
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/BufferBenchmark.kt
View File

@ -1,6 +1,6 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.benchmarks

48
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/DotBenchmark.kt
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@ -1,6 +1,6 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.benchmarks
@ -11,9 +11,10 @@ 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.linear.linearSpace
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.structures.Buffer
import kotlin.random.Random
@State(Scope.Benchmark)
@ -23,8 +24,12 @@ internal class DotBenchmark {
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 matrix1 = DoubleField.linearSpace.buildMatrix(dim, dim) { _, _ ->
random.nextDouble()
}
val matrix2 = DoubleField.linearSpace.buildMatrix(dim, dim) { _, _ ->
random.nextDouble()
}
val cmMatrix1 = CMLinearSpace { matrix1.toCM() }
val cmMatrix2 = CMLinearSpace { matrix2.toCM() }
@ -34,37 +39,32 @@ internal class DotBenchmark {
}
@Benchmark
fun cmDot(blackhole: Blackhole) {
CMLinearSpace.run {
blackhole.consume(cmMatrix1 dot cmMatrix2)
}
fun cmDotWithConversion(blackhole: Blackhole) = CMLinearSpace {
blackhole.consume(matrix1 dot matrix2)
}
@Benchmark
fun ejmlDot(blackhole: Blackhole) {
EjmlLinearSpaceDDRM {
blackhole.consume(ejmlMatrix1 dot ejmlMatrix2)
}
fun cmDot(blackhole: Blackhole) = CMLinearSpace {
blackhole.consume(cmMatrix1 dot cmMatrix2)
}
@Benchmark
fun ejmlDotWithConversion(blackhole: Blackhole) {
EjmlLinearSpaceDDRM {
blackhole.consume(matrix1 dot matrix2)
}
fun ejmlDot(blackhole: Blackhole) = EjmlLinearSpaceDDRM {
blackhole.consume(ejmlMatrix1 dot ejmlMatrix2)
}
@Benchmark
fun bufferedDot(blackhole: Blackhole) {
LinearSpace.auto(DoubleField).invoke {
blackhole.consume(matrix1 dot matrix2)
}
fun ejmlDotWithConversion(blackhole: Blackhole) = EjmlLinearSpaceDDRM {
blackhole.consume(matrix1 dot matrix2)
}
@Benchmark
fun realDot(blackhole: Blackhole) {
LinearSpace.real {
blackhole.consume(matrix1 dot matrix2)
}
fun bufferedDot(blackhole: Blackhole) = with(DoubleField.linearSpace(Buffer.Companion::auto)) {
blackhole.consume(matrix1 dot matrix2)
}
@Benchmark
fun doubleDot(blackhole: Blackhole) = with(DoubleField.linearSpace) {
blackhole.consume(matrix1 dot matrix2)
}
}

53
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ExpressionsInterpretersBenchmark.kt
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@ -1,6 +1,6 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.benchmarks
@ -11,27 +11,54 @@ import kotlinx.benchmark.Scope
import kotlinx.benchmark.State
import space.kscience.kmath.asm.compileToExpression
import space.kscience.kmath.expressions.*
import space.kscience.kmath.misc.Symbol
import space.kscience.kmath.misc.symbol
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
@ -44,23 +71,25 @@ internal class ExpressionsInterpretersBenchmark {
}
private companion object {
private val x: Symbol by symbol
private val algebra: DoubleField = DoubleField
private val x by symbol
private val algebra = DoubleField
private const val times = 1_000_000
private val functional: Expression<Double> = DoubleField.expressionInExtendedField {
bindSymbol(x) * number(2.0) + number(2.0) / bindSymbol(x) - number(16.0) / sin(bindSymbol(x))
private val functional = DoubleField.expression {
val x = bindSymbol(Symbol.x)
x * number(2.0) + 2.0 / x - 16.0 / sin(x)
}
private val node = MstExtendedField {
bindSymbol(x) * 2.0 + number(2.0) / bindSymbol(x) - number(16.0) / sin(bindSymbol(x))
x * 2.0 + number(2.0) / x - number(16.0) / sin(x)
}
private val mst: Expression<Double> = node.toExpression(DoubleField)
private val asm: Expression<Double> = node.compileToExpression(DoubleField)
private val mst = node.toExpression(DoubleField)
private val asm = node.compileToExpression(DoubleField)
private val raw: Expression<Double> = Expression { args ->
args.getValue(x) * 2.0 + 2.0 / args.getValue(x) - 16.0 / kotlin.math.sin(args.getValue(x))
private val raw = Expression<Double> { args ->
val x = args[x]!!
x * 2.0 + 2.0 / x - 16.0 / sin(x)
}
}
}

42
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/JafamaBenchmark.kt Normal file
View File

@ -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 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.contracts.InvocationKind
import kotlin.contracts.contract
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) {
contract { callsInPlace(expr, InvocationKind.AT_LEAST_ONCE) }
val rng = Random(0)
repeat(1000000) { blackhole.consume(expr(rng.nextDouble())) }
}

23
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/MatrixInverseBenchmark.kt
View File

@ -1,6 +1,6 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.benchmarks
@ -10,13 +10,12 @@ 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.commons.linear.lupSolver
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 space.kscience.kmath.linear.linearSpace
import space.kscience.kmath.linear.lupSolver
import space.kscience.kmath.operations.algebra
import kotlin.random.Random
@State(Scope.Benchmark)
@ -25,7 +24,7 @@ internal class MatrixInverseBenchmark {
private val random = Random(1224)
private const val dim = 100
private val space = LinearSpace.real
private val space = Double.algebra.linearSpace
//creating invertible matrix
private val u = space.buildMatrix(dim, dim) { i, j -> if (i <= j) random.nextDouble() else 0.0 }
@ -35,20 +34,20 @@ internal class MatrixInverseBenchmark {
@Benchmark
fun kmathLupInversion(blackhole: Blackhole) {
blackhole.consume(LinearSpace.real.inverseWithLup(matrix))
blackhole.consume(Double.algebra.linearSpace.lupSolver().inverse(matrix))
}
@Benchmark
fun cmLUPInversion(blackhole: Blackhole) {
with(CMLinearSpace) {
blackhole.consume(inverse(matrix))
CMLinearSpace {
blackhole.consume(lupSolver().inverse(matrix))
}
}
@Benchmark
fun ejmlInverse(blackhole: Blackhole) {
with(EjmlLinearSpaceDDRM) {
blackhole.consume(matrix.getFeature<InverseMatrixFeature<Double>>()?.inverse)
EjmlLinearSpaceDDRM {
blackhole.consume(matrix.toEjml().inverse())
}
}
}

69
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/NDFieldBenchmark.kt
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@ -1,6 +1,6 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.benchmarks
@ -9,45 +9,66 @@ 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.nd.StructureND
import space.kscience.kmath.nd.autoNdAlgebra
import space.kscience.kmath.nd.ndAlgebra
import space.kscience.kmath.nd4j.nd4j
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.tensors.core.DoubleTensor
import space.kscience.kmath.tensors.core.ones
import space.kscience.kmath.tensors.core.tensorAlgebra
@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)
}
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)
}
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)
}
fun boxingFieldAdd(blackhole: Blackhole) = with(genericField) {
var res: StructureND<Double> = one
repeat(n) { res += 1.0 }
blackhole.consume(res)
}
@Benchmark
fun tensorAdd(blackhole: Blackhole) = with(Double.tensorAlgebra) {
var res: DoubleTensor = ones(dim, dim)
repeat(n) { res = res + 1.0 }
blackhole.consume(res)
}
@Benchmark
fun tensorInPlaceAdd(blackhole: Blackhole) = with(Double.tensorAlgebra) {
val res: DoubleTensor = ones(dim, dim)
repeat(n) { res += 1.0 }
blackhole.consume(res)
}
// @Benchmark
// fun nd4jAdd(blackhole: Blackhole) = with(nd4jField) {
// 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)
private val autoField = DoubleField.autoNdAlgebra(dim, dim)
private val specializedField = DoubleField.ndAlgebra(dim, dim)
private val genericField = DoubleField.ndAlgebra(Buffer.Companion::boxing, dim, dim)
private val nd4jField = DoubleField.nd4j(dim, dim)
}
}

11
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ViktorBenchmark.kt
View File

@ -1,6 +1,6 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.benchmarks
@ -10,10 +10,9 @@ 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.nd.autoNdAlgebra
import space.kscience.kmath.nd.ndAlgebra
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.viktor.ViktorNDField
@ -59,8 +58,8 @@ internal class ViktorBenchmark {
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 autoField = DoubleField.autoNdAlgebra(dim, dim)
private val realField = DoubleField.ndAlgebra(dim, dim)
private val viktorField = ViktorNDField(dim, dim)
}
}

11
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ViktorLogBenchmark.kt
View File

@ -1,6 +1,6 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.benchmarks
@ -10,9 +10,8 @@ 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.nd.autoNdAlgebra
import space.kscience.kmath.nd.ndAlgebra
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.viktor.ViktorFieldND
@ -51,8 +50,8 @@ internal class ViktorLogBenchmark {
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 autoField = DoubleField.autoNdAlgebra(dim, dim)
private val realNdField = DoubleField.ndAlgebra(dim, dim)
private val viktorField = ViktorFieldND(intArrayOf(dim, dim))
}
}

53
build.gradle.kts
View File

@ -1,3 +1,5 @@
import java.net.URL
plugins {
id("ru.mipt.npm.gradle.project")
kotlin("jupyter.api") apply false
@ -7,15 +9,17 @@ allprojects {
repositories {
maven("https://clojars.org/repo")
maven("https://jitpack.io")
maven("http://logicrunch.research.it.uu.se/maven") {
isAllowInsecureProtocol = true
}
maven("https://maven.pkg.jetbrains.space/public/p/kotlinx-html/maven")
maven("https://oss.sonatype.org/content/repositories/snapshots")
mavenCentral()
}
group = "space.kscience"
version = "0.3.0-dev-8"
version = "0.3.0-dev-16"
}
subprojects {
@ -23,31 +27,46 @@ subprojects {
afterEvaluate {
tasks.withType<org.jetbrains.dokka.gradle.DokkaTaskPartial> {
dependsOn(tasks.getByName("assemble"))
dependsOn(tasks["assemble"])
dokkaSourceSets.all {
val readmeFile = File(this@subprojects.projectDir, "README.md")
if (readmeFile.exists()) includes.from(readmeFile.absolutePath)
externalDocumentationLink("http://ejml.org/javadoc/")
val readmeFile = this@subprojects.projectDir.resolve("README.md")
if (readmeFile.exists()) includes.from(readmeFile)
val kotlinDirPath = "src/$name/kotlin"
val kotlinDir = file(kotlinDirPath)
if (kotlinDir.exists()) sourceLink {
localDirectory.set(kotlinDir)
remoteUrl.set(
URL("https://github.com/mipt-npm/${rootProject.name}/tree/master/${this@subprojects.name}/$kotlinDirPath")
)
}
externalDocumentationLink("https://commons.apache.org/proper/commons-math/javadocs/api-3.6.1/")
externalDocumentationLink("https://deeplearning4j.org/api/latest/")
externalDocumentationLink("https://kotlin.github.io/kotlinx.coroutines/kotlinx-coroutines-core/")
externalDocumentationLink("https://breandan.net/kotlingrad/kotlingrad/", "https://breandan.net/kotlingrad/kotlingrad/kotlingrad/package-list")
externalDocumentationLink("https://axelclk.bitbucket.io/symja/javadoc/")
externalDocumentationLink(
"https://kotlin.github.io/kotlinx.coroutines/kotlinx-coroutines-core/",
"https://kotlin.github.io/kotlinx.coroutines/package-list",
)
externalDocumentationLink(
"https://breandan.net/kotlingrad/kotlingrad/",
"https://breandan.net/kotlingrad/kotlingrad/kotlingrad/package-list",
)
}
}
}
}
readme {
readmeTemplate = file("docs/templates/README-TEMPLATE.md")
}
readme.readmeTemplate = file("docs/templates/README-TEMPLATE.md")
ksciencePublish {
github("kmath")
space()
sonatype()
vcs("https://github.com/mipt-npm/kmath")
space(publish = true)
sonatype(publish = true)
}
apiValidation {
nonPublicMarkers.add("space.kscience.kmath.misc.UnstableKMathAPI")
}
apiValidation.nonPublicMarkers.add("space.kscience.kmath.misc.UnstableKMathAPI")

20
buildSrc/build.gradle.kts Normal file
View File

@ -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:0.10.2")
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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@ -0,0 +1,60 @@
/*
* 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 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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@ -0,0 +1,100 @@
/*
* 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 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
View File

@ -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 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 : $ejmlMatrixType>(override val origin: M) : EjmlVector<$type, M>(origin) {
init {
require(origin.numRows == 1) { "The origin matrix must have only one row to form a vector" }
}
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 : $ejmlMatrixType>(override val origin: M) : EjmlMatrix<$type, M>(origin) {
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.
*/
override val elementAlgebra: $kmathAlgebra get() = $kmathAlgebra
@Suppress("UNCHECKED_CAST")
override fun Matrix<${type}>.toEjml(): Ejml${type}Matrix<${ejmlMatrixType}> = when {
this is Ejml${type}Matrix<*> && origin is $ejmlMatrixType -> this as Ejml${type}Matrix<${ejmlMatrixType}>
else -> buildMatrix(rowNum, colNum) { i, j -> get(i, j) }
}
@Suppress("UNCHECKED_CAST")
override fun Point<${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) }
})
}
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()
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)
override fun Matrix<${type}>.unaryMinus(): Matrix<${type}> = this * elementAlgebra { -one }
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()
}
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()
}
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,${
if (isDense) "" else
"""
null,
null,"""
}
)
return out.wrapMatrix()
}
override operator fun Matrix<${type}>.times(value: ${type}): Ejml${type}Matrix<${ejmlMatrixType}> {
val res = ${ejmlMatrixType}(1, 1)
CommonOps_${ops}.scale(value, toEjml().origin, res)
return res.wrapMatrix()
}
override fun Point<${type}>.unaryMinus(): Ejml${type}Vector<${ejmlMatrixType}> {
val res = ${ejmlMatrixType}(1, 1)
CommonOps_${ops}.changeSign(toEjml().origin, res)
return res.wrapVector()
}
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,${
if (isDense) "" else
"""
null,
null,"""
}
)
return out.wrapMatrix()
}
override fun Point<${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,${
if (isDense) "" else
"""
null,
null,"""
}
)
return out.wrapVector()
}
override fun Point<${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()
}
override fun ${type}.times(m: Matrix<${type}>): Ejml${type}Matrix<${ejmlMatrixType}> = m * this
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()
}
override fun ${type}.times(v: Point<${type}>): Ejml${type}Vector<${ejmlMatrixType}> = v * this
@UnstableKMathAPI
override fun <F : StructureFeature> computeFeature(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().withFeature(OrthogonalFeature)
}
override val r: Matrix<${type}> by lazy { qr.getR(null, false).wrapMatrix().withFeature(UFeature) }
}
CholeskyDecompositionFeature::class -> object : CholeskyDecompositionFeature<${type}> {
override val l: Matrix<${type}> by lazy {
val cholesky =
DecompositionFactory_${ops}.chol(structure.rowNum, true).apply { decompose(origin.copy()) }
cholesky.getT(null).wrapMatrix().withFeature(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<${type}> by lazy {
lup.getLower(null).wrapMatrix().withFeature(LFeature)
}
override val u: Matrix<${type}> by lazy {
lup.getUpper(null).wrapMatrix().withFeature(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().withFeature(OrthogonalFeature)
}
override val r: Matrix<${type}> by lazy { qr.getR(null, false).wrapMatrix().withFeature(UFeature) }
}
CholeskyDecompositionFeature::class -> object : CholeskyDecompositionFeature<${type}> {
override val l: Matrix<${type}> by lazy {
val cholesky =
DecompositionFactory_${ops}.cholesky().apply { decompose(origin.copy()) }
(cholesky.getT(null) as ${ejmlMatrixParentTypeMatrix}).wrapMatrix().withFeature(LFeature)
}
}
LUDecompositionFeature::class, DeterminantFeature::class, InverseMatrixFeature::class -> object :
LUDecompositionFeature<${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().withFeature(LFeature)
}
override val u: Matrix<${type}> by lazy {
lu.getUpper(null).wrapMatrix().withFeature(UFeature)
}
override val inverse: Matrix<${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 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,
)
}
}

83
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@ -1,85 +1,45 @@
# Algebraic Structures and Algebraic Elements
The mathematical operations in KMath are generally separated from mathematical objects. This means that to perform an
operation, say `+`, one needs two objects of a type `T` and an algebra context, which draws appropriate operation up,
say `Space<T>`. Next one needs to run the actual operation in the context:
The mathematical operations in KMath are generally separated from mathematical objects. This means that to perform an
operation, say `+`, one needs two objects of a type `T` and an algebra context, which draws appropriate operation up,
say `Group<T>`. Next one needs to run the actual operation in the context:
```kotlin
import space.kscience.kmath.operations.*
val a: T = ...
val b: T = ...
val space: Space<T> = ...
val group: Group<T> = ...
val c = space { a + b }
val c = group { a + b }
```
At first glance, this distinction seems to be a needless complication, but in fact one needs to remember that in
mathematics, one could draw up different operations on same objects. For example, one could use different types of
At first glance, this distinction seems to be a needless complication, but in fact one needs to remember that in
mathematics, one could draw up different operations on same objects. For example, one could use different types of
geometry for vectors.
## Algebraic Structures
Mathematical contexts have the following hierarchy:
Primary mathematical contexts have the following hierarchy:
**Algebra** ← **Space** ← **Ring** ← **Field**
`Field <: Ring <: Group <: Algebra`
These interfaces follow real algebraic structures:
- [Space](https://mathworld.wolfram.com/VectorSpace.html) defines addition, its neutral element (i.e. 0) and scalar
multiplication;
- [Ring](http://mathworld.wolfram.com/Ring.html) adds multiplication and its neutral element (i.e. 1);
- [Group](https://mathworld.wolfram.com/Group.html) defines addition, its identity element (i.e., 0) and additive
inverse (-x);
- [Ring](http://mathworld.wolfram.com/Ring.html) adds multiplication and its identity element (i.e., 1);
- [Field](http://mathworld.wolfram.com/Field.html) adds division operation.
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`
operation produces a matrix with new dimensions, which can be incompatible with initial matrix in terms of linear
operations.
## Algebraic Element
To achieve more familiar behavior (where you apply operations directly to mathematical objects), without involving
contexts KMath submits special type objects called `MathElement`. A `MathElement` is basically some object coupled to
a mathematical context. For example `Complex` is the pair of real numbers representing real and imaginary parts,
but it also holds reference to the `ComplexField` singleton, which allows performing direct operations on `Complex`
numbers without explicit involving the context like:
```kotlin
import space.kscience.kmath.operations.*
// Using elements
val c1 = Complex(1.0, 1.0)
val c2 = Complex(1.0, -1.0)
val c3 = c1 + c2 + 3.0.toComplex()
// Using context
val c4 = ComplexField { c1 + i - 2.0 }
```
Both notations have their pros and cons.
The hierarchy for algebraic elements follows the hierarchy for the corresponding algebraic structures.
**MathElement** ← **SpaceElement** ← **RingElement** ← **FieldElement**
`MathElement<C>` is the generic common ancestor of the class with context.
One major distinction between algebraic elements and algebraic contexts is that elements have three type
parameters:
1. The type of elements, the field operates on.
2. The self-type of the element returned from operation (which has to be an algebraic element).
3. The type of the algebra over first type-parameter.
The middle type is needed for of algebra members do not store context. For example, it is impossible to add a context
to regular `Double`. The element performs automatic conversions from context types and back. One should use context
operations in all performance-critical places. The performance of element operations is not guaranteed.
interface. Also, contexts may have operations, which produce elements outside the context. For example, `Matrix.dot`
operation produces a matrix with new dimensions, which can be incompatible with initial matrix in linear operations.
## Spaces and Fields
KMath submits both contexts and elements for builtin algebraic structures:
KMath introduces contexts for builtin algebraic structures:
```kotlin
import space.kscience.kmath.operations.*
@ -102,13 +62,13 @@ val c2 = ComplexField { c1 - 1.0 } // Returns: Complex(re=0.0, im=2.0)
val c3 = ComplexField { c1 - i * 2.0 }
```
**Note**: In theory it is possible to add behaviors directly to the context, but as for now Kotlin does not support
that. Watch [KT-10468](https://youtrack.jetbrains.com/issue/KT-10468) and
**Note**: In theory it is possible to add behaviors directly to the context, but as for now Kotlin does not support
that. Watch [KT-10468](https://youtrack.jetbrains.com/issue/KT-10468) and
[KEEP-176](https://github.com/Kotlin/KEEP/pull/176) for updates.
## Nested fields
Contexts allow one to build more complex structures. For example, it is possible to create a `Matrix` from complex
Contexts allow one to build more complex structures. For example, it is possible to create a `Matrix` from complex
elements like so:
```kotlin
@ -118,8 +78,9 @@ val element = NDElement.complex(shape = intArrayOf(2, 2)) { index: IntArray ->
```
The `element` in this example is a member of the `Field` of 2D structures, each element of which is a member of its own
`ComplexField`. It is important one does not need to create a special n-d class to hold complex
numbers and implement operations on it, one just needs to provide a field for its elements.
`ComplexField`. It is important one does not need to create a special n-d class to hold complex numbers and implement
operations on it, one just needs to provide a field for its elements.
**Note**: Fields themselves do not solve the problem of JVM boxing, but it is possible to solve with special contexts like
**Note**: Fields themselves do not solve the problem of JVM boxing, but it is possible to solve with special contexts
like
`MemorySpec`.

17
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# Buffers
Buffer is one of main building blocks of kmath. It is a basic interface allowing random-access read and write (with `MutableBuffer`).
There are different types of buffers:
Buffer is one of main building blocks of kmath. It is a basic interface allowing random-access read and write (
with `MutableBuffer`). There are different types of buffers:
* Primitive buffers wrapping like `RealBuffer` which are wrapping primitive arrays.
* Primitive buffers wrapping like `DoubleBuffer` which are wrapping primitive arrays.
* Boxing `ListBuffer` wrapping a list
* Functionally defined `VirtualBuffer` which does not hold a state itself, but provides a function to calculate value
* `MemoryBuffer` allows direct allocation of objects in continuous memory block.
Some kmath features require a `BufferFactory` class to operate properly. A general convention is to use functions defined in
`Buffer` and `MutableBuffer` companion classes. For example factory `Buffer.Companion::auto` in most cases creates the most suitable
buffer for given reified type (for types with custom memory buffer it still better to use their own `MemoryBuffer.create()` factory).
Some kmath features require a `BufferFactory` class to operate properly. A general convention is to use functions
defined in
`Buffer` and `MutableBuffer` companion classes. For example factory `Buffer.Companion::auto` in most cases creates the
most suitable buffer for given reified type (for types with custom memory buffer it still better to use their
own `MemoryBuffer.create()` factory).
## Buffer performance
One should avoid using default boxing buffer wherever it is possible. Try to use primitive buffers or memory buffers instead
One should avoid using default boxing buffer wherever it is possible. Try to use primitive buffers or memory buffers
instead .

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# Coding Conventions
KMath code follows general [Kotlin conventions](https://kotlinlang.org/docs/reference/coding-conventions.html), but
with a number of small changes and clarifications.
Generally, KMath code follows general [Kotlin coding conventions](https://kotlinlang.org/docs/reference/coding-conventions.html), but with a number of small changes and clarifications.
## Utility Class Naming
Filename should coincide with a name of one of the classes contained in the file or start with small letter and
describe its contents.
Filename should coincide with a name of one of the classes contained in the file or start with small letter and describe its contents.
The code convention [here](https://kotlinlang.org/docs/reference/coding-conventions.html#source-file-names) says that
file names should start with a capital letter even if file does not contain classes. Yet starting utility classes and
aggregators with a small letter seems to be a good way to visually separate those files.
The code convention [here](https://kotlinlang.org/docs/reference/coding-conventions.html#source-file-names) says that file names should start with a capital letter even if file does not contain classes. Yet starting utility classes and aggregators with a small letter seems to be a good way to visually separate those files.
This convention could be changed in future in a non-breaking way.
## Private Variable Naming
Private variables' names may start with underscore `_` for of the private mutable variable is shadowed by the public
read-only value with the same meaning.
Private variables' names may start with underscore `_` for of the private mutable variable is shadowed by the public read-only value with the same meaning.
This rule does not permit underscores in names, but it is sometimes useful to "underscore" the fact that public and
private versions draw up the same entity. It is allowed only for private variables.
This rule does not permit underscores in names, but it is sometimes useful to "underscore" the fact that public and private versions draw up the same entity. It is allowed only for private variables.
This convention could be changed in future in a non-breaking way.
@ -30,5 +24,4 @@ Use one-liners when they occupy single code window line both for functions and p
`val b: String get() = "fff"`. The same should be performed with multiline expressions when they could be
cleanly separated.
There is no universal consensus whenever use `fun a() = ...` or `fun a() { return ... }`. Yet from reader outlook
one-lines seem to better show that the property or function is easily calculated.
There is no universal consensus whenever use `fun a() = ...` or `fun a() { return ... }`. Yet from reader outlook one-lines seem to better show that the property or function is easily calculated.

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## The problem
A known problem for implementing mathematics in statically-typed languages (but not only in them) is that different
sets of mathematical operators can be defined on the same mathematical objects. Sometimes there is no single way to
treat some operations, including basic arithmetic operations, on a Java/Kotlin `Number`. Sometimes there are different ways to
define the same structure, such as Euclidean and elliptic geometry vector spaces over real vectors. Another problem arises when
one wants to add some kind of behavior to an existing entity. In dynamic languages those problems are usually solved
by adding dynamic context-specific behaviors at runtime, but this solution has a lot of drawbacks.
A known problem for implementing mathematics in statically-typed languages (but not only in them) is that different sets
of mathematical operators can be defined on the same mathematical objects. Sometimes there is no single way to treat
some operations, including basic arithmetic operations, on a Java/Kotlin `Number`. Sometimes there are different ways to
define the same structure, such as Euclidean and elliptic geometry vector spaces over real vectors. Another problem
arises when one wants to add some kind of behavior to an existing entity. In dynamic languages those problems are
usually solved by adding dynamic context-specific behaviors at runtime, but this solution has a lot of drawbacks.
## Context-oriented approach
One possible solution to these problems is to divorce numerical representations from behaviors.
For example in Kotlin one can define a separate class which represents some entity without any operations,
ex. a complex number:
One possible solution to these problems is to divorce numerical representations from behaviors. For example in Kotlin
one can define a separate class representing some entity without any operations, ex. a complex number:
```kotlin
data class Complex(val re: Double, val im: Double)
@ -28,9 +27,10 @@ object ComplexOperations {
}
```
In Java, applying such external operations could be very cumbersome, but Kotlin has a unique feature which allows us
implement this naturally: [extensions with receivers](https://kotlinlang.org/docs/reference/extensions.html#extension-functions).
In Kotlin, an operation on complex number could be implemented as:
In Java, applying such external operations could be cumbersome, but Kotlin has a unique feature that allows us
implement this
naturally: [extensions with receivers](https://kotlinlang.org/docs/reference/extensions.html#extension-functions). In
Kotlin, an operation on complex number could be implemented as:
```kotlin
with(ComplexOperations) { c1 + c2 - c3 }
@ -52,20 +52,20 @@ In KMath, contexts are not only responsible for operations, but also for raw obj
### Type classes
An obvious candidate to get more or less the same functionality is the type class, which allows one to bind a behavior to
a specific type without modifying the type itself. On the plus side, type classes do not require explicit context
An obvious candidate to get more or less the same functionality is the type class, which allows one to bind a behavior
to a specific type without modifying the type itself. On the plus side, type classes do not require explicit context
declaration, so the code looks cleaner. On the minus side, if there are different sets of behaviors for the same types,
it is impossible to combine them into one module. Also, unlike type classes, context can have parameters or even
state. For example in KMath, sizes and strides for `NDElement` or `Matrix` could be moved to context to optimize
performance in case of a large amount of structures.
it is impossible to combine them into one module. Also, unlike type classes, context can have parameters or even state.
For example in KMath, sizes and strides for `NDElement` or `Matrix` could be moved to context to optimize performance in
case of a large amount of structures.
### Wildcard imports and importing-on-demand
Sometimes, one may wish to use a single context throughout a file. In this case, is possible to import all members
from a package or file, via `import context.complex.*`. Effectively, this is the same as enclosing an entire file
with a single context. However when using multiple contexts, this technique can introduce operator ambiguity, due to
namespace pollution. If there are multiple scoped contexts which define the same operation, it is still possible to
to import specific operations as needed, without using an explicit context with extension functions, for example:
Sometimes, one may wish to use a single context throughout a file. In this case, is possible to import all members from
a package or file, via `import context.complex.*`. Effectively, this is the same as enclosing an entire file with a
single context. However, when using multiple contexts, this technique can introduce operator ambiguity, due to namespace
pollution. If there are multiple scoped contexts that define the same operation, it is still possible to import
specific operations as needed, without using an explicit context with extension functions, for example:
```
import context.complex.op1

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# Expressions
**Experimental: this API is in early stage and could change any time**
Expressions is an experimental feature which allows to construct lazily or immediately calculated parametric mathematical
expressions.
Expressions is a feature, which allows constructing lazily or immediately calculated parametric mathematical expressions.
The potential use-cases for it (so far) are following:
* Lazy evaluation (in general simple lambda is better, but there are some border cases)
* lazy evaluation (in general simple lambda is better, but there are some border cases);
* automatic differentiation in single-dimension and in multiple dimensions;
* generation of mathematical syntax trees with subsequent code generation for other languages;
* symbolic computations, especially differentiation (and some other actions with `kmath-symja` integration with Symja's `IExpr`&mdash;integration, simplification, and more);
* visualization with `kmath-jupyter`.
* Automatic differentiation in single-dimension and in multiple dimensions
* Generation of mathematical syntax trees with subsequent code generation for other languages
* Maybe symbolic computations (needs additional research)
The workhorse of this API is `Expression` interface which exposes single `operator fun invoke(arguments: Map<String, T>): T`
method. `ExpressionContext` is used to generate expressions and introduce variables.
The workhorse of this API is `Expression` interface, which exposes single `operator fun invoke(arguments: Map<Symbol, T>): T`
method. `ExpressionAlgebra` is used to generate expressions and introduce variables.
Currently there are two implementations:
* Generic `ExpressionField` in `kmath-core` which allows construction of custom lazy expressions
* Auto-differentiation expression in `kmath-commons` module allows to use full power of `DerivativeStructure`
* Auto-differentiation expression in `kmath-commons` module allows using full power of `DerivativeStructure`
from commons-math. **TODO: add example**

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# Features
* [Algebra](algebra.md) - [Context-based](contexts.md) operations on different primitives and structures.
* [NDStructures](nd-structure.md)
* [Linear algebra](linear.md) - Matrices, operations and linear equations solving. To be moved to separate module. Currently supports basic
api and multiple library back-ends.
* [Histograms](histograms.md) - Multidimensional histogram calculation and operations.
* [Expressions](expressions.md)
* Commons math integration

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@ -1,4 +1,9 @@
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<!--
- 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 file.
-->
<svg
xmlns:dc="http://purl.org/dc/elements/1.1/"
xmlns:cc="http://creativecommons.org/ns#"

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<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<!--
- 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 file.
-->
<svg
xmlns:dc="http://purl.org/dc/elements/1.1/"
xmlns:cc="http://creativecommons.org/ns#"

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<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<!--
- 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 file.
-->
<svg
xmlns:dc="http://purl.org/dc/elements/1.1/"
xmlns:cc="http://creativecommons.org/ns#"

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<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<!--
- 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 file.
-->
<svg
xmlns:dc="http://purl.org/dc/elements/1.1/"
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## Basic linear algebra layout
KMath support for linear algebra organized in a context-oriented way. Meaning that operations are in most cases declared
in context classes, and are not the members of classes that store data. This allows more flexible approach to maintain multiple
back-ends. The new operations added as extensions to contexts instead of being member functions of data structures.
KMath support for linear algebra organized in a context-oriented way, which means that operations are in most cases declared in context classes, and are not the members of classes that store data. This allows more flexible approach to maintain multiple back-ends. The new operations added as extensions to contexts instead of being member functions of data structures.
Two major contexts used for linear algebra and hyper-geometry:
The main context for linear algebra over matrices and vectors is `LinearSpace`, which defines addition and dot products of matrices and vectors:
* `VectorSpace` forms a mathematical space on top of array-like structure (`Buffer` and its type alias `Point` used for geometry).
```kotlin
import space.kscience.kmath.linear.*
* `MatrixContext` forms a space-like context for 2d-structures. It does not store matrix size and therefore does not implement
`Space` interface (it is impossible to create zero element without knowing the matrix size).
LinearSpace.Companion.real {
val vec = buildVector(10) { i -> i.toDouble() }
val mat = buildMatrix(10, 10) { i, j -> i.toDouble() + j }
## Vector spaces
// Addition
vec + vec
mat + mat
// Multiplication by scalar
vec * 2.0
mat * 2.0
## Matrix operations
// Dot product
mat dot vec
mat dot mat
}
```
## Back-end overview
## Backends overview
### EJML
### Commons Math

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@ -11,16 +11,16 @@ Let us consider following contexts:
```kotlin
// automatically build context most suited for given type.
val autoField = NDField.auto(DoubleField, dim, dim)
// specialized nd-field for Double. It works as generic Double field as well
// 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(DoubleField, dim, dim)
```
Now let us perform several tests and see which implementation is best suited for each case:
Now let us perform several tests and see, which implementation is best suited for each case:
## Test case
In order to test performance we will take 2d-structures with `dim = 1000` and add a structure filled with `1.0`
To test performance we will take 2d-structures with `dim = 1000` and add a structure filled with `1.0`
to it `n = 1000` times.
## Specialized
@ -35,8 +35,8 @@ The code to run this looks like:
```
The performance of this code is the best of all tests since it inlines all operations and is specialized for operation
with doubles. We will measure everything else relative to this one, so time for this test will be `1x` (real time
on my computer is about 4.5 seconds). The only problem with this approach is that it requires to specify type
from the beginning. Everyone do so anyway, so it is the recommended approach.
on my computer is about 4.5 seconds). The only problem with this approach is that it requires specifying type
from the beginning. Everyone does so anyway, so it is the recommended approach.
## Automatic
Let's do the same with automatic field inference:
@ -49,7 +49,7 @@ Let's do the same with automatic field inference:
}
```
Ths speed of this operation is approximately the same as for specialized case since `NDField.auto` just
returns the same `RealNDField` in this case. Of course it is usually better to use specialized method to be sure.
returns the same `RealNDField` in this case. Of course, it is usually better to use specialized method to be sure.
## Lazy
Lazy field does not produce a structure when asked, instead it generates an empty structure and fills it on-demand
@ -63,7 +63,7 @@ When one calls
}
}
```
The result will be calculated almost immediately but the result will be empty. In order to get the full result
The result will be calculated almost immediately but the result will be empty. To get the full result
structure one needs to call all its elements. In this case computation overhead will be huge. So this field never
should be used if one expects to use the full result structure. Though if one wants only small fraction, it could
save a lot of time.
@ -94,7 +94,7 @@ The boxing field produced by
}
}
```
obviously is the slowest one, because it requires to box and unbox the `double` on each operation. It takes about
is the slowest one, because it requires boxing and unboxing the `double` on each operation. It takes about
`15x` time (**TODO: there seems to be a problem here, it should be slow, but not that slow**). This field should
never be used for primitives.
@ -115,12 +115,14 @@ via extension function.
Usually it is bad idea to compare the direct numerical operation performance in different languages, but it hard to
work completely without frame of reference. In this case, simple numpy code:
```python
import numpy as np
res = np.ones((1000,1000))
for i in range(1000):
res = res + 1.0
```
gives the completion time of about `1.1x`, which means that specialized kotlin code in fact is working faster (I think it is
because better memory management). Of course if one writes `res += 1.0`, the performance will be different,
but it would be differenc case, because numpy overrides `+=` with in-place operations. In-place operations are
but it would be different case, because numpy overrides `+=` with in-place operations. In-place operations are
available in `kmath` with `MutableNDStructure` but there is no field for it (one can still work with mapping
functions).

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@ -0,0 +1,14 @@
# Documentation
* [Algebra](algebra.md): [context-based](contexts.md) operations on different primitives and structures.
* [NDStructures](nd-structure.md)
* [Linear algebra](linear.md): matrices, operations and linear equations solving. To be moved to separate module.
Currently, supports basic API and multiple library back-ends.
* [Histograms](histograms.md): multidimensional histogram calculation and operations.
* [Expressions](expressions.md)
* Commons math integration

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@ -2,14 +2,14 @@
[![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)
[![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/maven-metadata/v?label=Space&metadataUrl=https%3A%2F%2Fmaven.pkg.jetbrains.space%2Fmipt-npm%2Fp%2Fsci%2Fmaven%2Fkscience%2Fkmath%2Fkmath-core%2Fmaven-metadata.xml)](https://maven.pkg.jetbrains.space/mipt-npm/p/sci/maven/space/kscience/)
[![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
Could be pronounced as `key-math`. The **K**otlin **Math**ematics library was initially intended as a Kotlin-based analog to
Python's NumPy library. Later we found that kotlin is much more flexible language and allows superior architecture
designs. In contrast to `numpy` and `scipy` it is modular and has a lightweight core. The `numpy`-like experience could
be achieved with [kmath-for-real](/kmath-for-real) extension module.
Could be pronounced as `key-math`. The **K**otlin **Math**ematics library was initially intended as a Kotlin-based
analog to Python's NumPy library. Later we found that kotlin is much more flexible language and allows superior
architecture designs. In contrast to `numpy` and `scipy` it is modular and has a lightweight core. The `numpy`-like
experience could be achieved with [kmath-for-real](/kmath-for-real) extension module.
[Documentation site (**WIP**)](https://mipt-npm.github.io/kmath/)
@ -21,26 +21,33 @@ be achieved with [kmath-for-real](/kmath-for-real) extension module.
# Goal
* Provide a flexible and powerful API to work with mathematics abstractions in Kotlin-multiplatform (JVM, JS and Native).
* Provide a flexible and powerful API to work with mathematics abstractions in Kotlin-multiplatform (JVM, JS and Native)
.
* Provide basic multiplatform implementations for those abstractions (without significant performance optimization).
* Provide bindings and wrappers with those abstractions for popular optimized platform libraries.
## Non-goals
* Be like NumPy. It was the idea at the beginning, but we decided that we can do better in terms of API.
* Be like NumPy. It was the idea at the beginning, but we decided that we can do better in API.
* Provide the best performance out of the box. We have specialized libraries for that. Need only API wrappers for them.
* Cover all cases as immediately and in one bundle. We will modularize everything and add new features gradually.
* Provide specialized behavior in the core. API is made generic on purpose, so one needs to specialize for types, like
for `Double` in the core. For that we will have specialization modules like `kmath-for-real`, which will give better
experience for those, who want to work with specific types.
* Provide specialized behavior in the core. API is made generic on purpose, so one needs to specialize for types, like
for `Double` in the core. For that we will have specialization modules like `kmath-for-real`, which will give better
experience for those, who want to work with specific types.
## Features and stability
KMath is a modular library. Different modules provide different features with different API stability guarantees. All core modules are released with the same version, but with different API change policy. The features are described in module definitions below. The module stability could have following levels:
KMath is a modular library. Different modules provide different features with different API stability guarantees. All
core modules are released with the same version, but with different API change policy. The features are described in
module definitions below. The module stability could have the following levels:
* **PROTOTYPE**. On this level there are no compatibility guarantees. All methods and classes form those modules could break any moment. You can still use it, but be sure to fix the specific version.
* **EXPERIMENTAL**. The general API is decided, but some changes could be made. Volatile API is marked with `@UnstableKmathAPI` or other stability warning annotations.
* **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.
* **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 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)-->
@ -78,30 +85,33 @@ $modules
## Multi-platform support
KMath is developed as a multi-platform library, which means that most of the interfaces are declared in the
[common source sets](/kmath-core/src/commonMain) and implemented there wherever it is possible. In some cases, features
are delegated to platform-specific implementations even if they could be provided in the common module for performance
reasons. Currently, the Kotlin/JVM is the primary platform, however Kotlin/Native and Kotlin/JS contributions and
KMath is developed as a multi-platform library, which means that most of the interfaces are declared in the
[common source sets](/kmath-core/src/commonMain) and implemented there wherever it is possible. In some cases, features
are delegated to platform-specific implementations even if they could be provided in the common module for performance
reasons. Currently, the Kotlin/JVM is the primary platform, however Kotlin/Native and Kotlin/JS contributions and
feedback are also welcome.
## Performance
Calculation performance is one of major goals of KMath in the future, but in some cases it is impossible to achieve
both performance and flexibility.
Calculation performance is one of major goals of KMath in the future, but in some cases it is impossible to achieve both
performance and flexibility.
We expect to focus on creating convenient universal API first and then work on increasing performance for specific
cases. We expect the worst KMath benchmarks will perform better than native Python, but worse than optimized
native/SciPy (mostly due to boxing operations on primitive numbers). The best performance of optimized parts could be
We expect to focus on creating convenient universal API first and then work on increasing performance for specific
cases. We expect the worst KMath benchmarks will perform better than native Python, but worse than optimized
native/SciPy (mostly due to boxing operations on primitive numbers). The best performance of optimized parts could be
better than SciPy.
## 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.
KMath currently relies on JDK 11 for compilation and execution of Kotlin-JVM part. We recommend to use GraalVM-CE 11 for
execution 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
[Kotlin Multiplatform](https://kotlinlang.org/docs/reference/multiplatform.html) for more details). The repository could be reached through [repo.kotlin.link](https://repo.kotlin.link) proxy:
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
[Kotlin Multiplatform](https://kotlinlang.org/docs/reference/multiplatform.html) for more details). The repository could
be reached through [repo.kotlin.link](https://repo.kotlin.link) proxy:
```kotlin
repositories {
@ -118,7 +128,7 @@ Gradle `6.0+` is required for multiplatform artifacts.
## Contributing
The project requires a lot of additional work. The most important thing we need is a feedback about what features are
required the most. Feel free to create feature requests. We are also welcome to code contributions,
especially in issues marked with
The project requires a lot of additional work. The most important thing we need is a feedback about what features are
required the most. Feel free to create feature requests. We are also welcome to code contributions, especially in issues
marked with
[waiting for a hero](https://github.com/mipt-npm/kmath/labels/waiting%20for%20a%20hero) label.

11
examples/build.gradle.kts
View File

@ -20,13 +20,14 @@ dependencies {
implementation(project(":kmath-coroutines"))
implementation(project(":kmath-commons"))
implementation(project(":kmath-complex"))
implementation(project(":kmath-optimization"))
implementation(project(":kmath-stat"))
implementation(project(":kmath-viktor"))
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.nd4j:nd4j-native:1.0.0-beta7")
@ -41,9 +42,11 @@ dependencies {
// } else
implementation("org.nd4j:nd4j-native-platform:1.0.0-beta7")
implementation("org.slf4j:slf4j-simple:1.7.30")
implementation("org.slf4j:slf4j-simple:1.7.31")
// plotting
implementation("space.kscience:plotlykt-server:0.4.0-dev-2")
implementation("space.kscience:plotlykt-server:0.4.2")
//jafama
implementation(project(":kmath-jafama"))
}
kotlin.sourceSets.all {
@ -57,7 +60,7 @@ kotlin.sourceSets.all {
tasks.withType<org.jetbrains.kotlin.gradle.tasks.KotlinCompile> {
kotlinOptions{
jvmTarget = "11"
freeCompilerArgs = freeCompilerArgs + "-Xjvm-default=all"
freeCompilerArgs = freeCompilerArgs + "-Xjvm-default=all" + "-Xopt-in=kotlin.RequiresOptIn"
}
}

4
examples/src/main/kotlin/space/kscience/kmath/ast/astRendering.kt
View File

@ -1,6 +1,6 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.ast
@ -10,7 +10,7 @@ import space.kscience.kmath.ast.rendering.LatexSyntaxRenderer
import space.kscience.kmath.ast.rendering.MathMLSyntaxRenderer
import space.kscience.kmath.ast.rendering.renderWithStringBuilder
public fun main() {
fun main() {
val mst = "exp(sqrt(x))-asin(2*x)/(2e10+x^3)/(-12)".parseMath()
val syntax = FeaturedMathRendererWithPostProcess.Default.render(mst)
println("MathSyntax:")

6
examples/src/main/kotlin/space/kscience/kmath/ast/expressions.kt
View File

@ -1,20 +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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.ast
import space.kscience.kmath.expressions.MstField
import space.kscience.kmath.expressions.Symbol.Companion.x
import space.kscience.kmath.expressions.interpret
import space.kscience.kmath.misc.Symbol.Companion.x
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.bindSymbol
import space.kscience.kmath.operations.invoke
fun main() {
val expr = MstField {
val x = bindSymbol(x)
x * 2.0 + number(2.0) / x - 16.0
}

24
examples/src/main/kotlin/space/kscience/kmath/ast/kotlingradSupport.kt
View File

@ -1,29 +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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.ast
import space.kscience.kmath.asm.compileToExpression
import space.kscience.kmath.expressions.derivative
import space.kscience.kmath.expressions.invoke
import space.kscience.kmath.kotlingrad.toDiffExpression
import space.kscience.kmath.misc.symbol
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.
* In this example, *x<sup>2</sup> &minus; 4 x &minus; 44* function is differentiated with Kotlin∇, and the
* derivation result is compared with valid derivative in a certain point.
*/
fun main() {
val x by symbol
val actualDerivative = "x^2-4*x-44".parseMath()
.toDiffExpression(DoubleField)
val actualDerivative = "x^2-4*x-44"
.parseMath()
.toKotlingradExpression(DoubleField)
.derivative(x)
val expectedDerivative = "2*x-4".parseMath().compileToExpression(DoubleField)
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
View File

@ -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 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<sup>2</sup> &minus; 4 x &minus; 44* function is differentiated with Symja, and the
* derivation 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))
}

42
examples/src/main/kotlin/space/kscience/kmath/commons/fit/fitWithAutoDiff.kt → examples/src/main/kotlin/space/kscience/kmath/fit/chiSquared.kt
View File

@ -1,32 +1,34 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.commons.fit
package space.kscience.kmath.fit
import kotlinx.html.br
import kotlinx.html.h3
import space.kscience.kmath.commons.optimization.chiSquared
import space.kscience.kmath.commons.optimization.minimize
import space.kscience.kmath.commons.expressions.DSProcessor
import space.kscience.kmath.commons.optimization.CMOptimizer
import space.kscience.kmath.distributions.NormalDistribution
import space.kscience.kmath.misc.symbol
import space.kscience.kmath.optimization.FunctionOptimization
import space.kscience.kmath.optimization.OptimizationResult
import space.kscience.kmath.expressions.chiSquaredExpression
import space.kscience.kmath.expressions.symbol
import space.kscience.kmath.operations.asIterable
import space.kscience.kmath.operations.toList
import space.kscience.kmath.optimization.FunctionOptimizationTarget
import space.kscience.kmath.optimization.optimizeWith
import space.kscience.kmath.optimization.resultPoint
import space.kscience.kmath.optimization.resultValue
import space.kscience.kmath.real.DoubleVector
import space.kscience.kmath.real.map
import space.kscience.kmath.real.step
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
//Forward declaration of symbols that will be used in expressions.
// This declaration is required for
// Forward declaration of symbols that will be used in expressions.
private val a by symbol
private val b by symbol
private val c by symbol
@ -43,7 +45,7 @@ operator fun TraceValues.invoke(vector: DoubleVector) {
*/
suspend fun main() {
//A generator for a normally distributed values
val generator = NormalDistribution(2.0, 7.0)
val generator = NormalDistribution(0.0, 1.0)
//A chain/flow of random values with the given seed
val chain = generator.sample(RandomGenerator.default(112667))
@ -54,7 +56,7 @@ suspend fun main() {
//Perform an operation on each x value (much more effective, than numpy)
val y = x.map {
val y = x.map { it ->
val value = it.pow(2) + it + 1
value + chain.next() * sqrt(value)
}
@ -65,17 +67,21 @@ suspend 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 = FunctionOptimization.chiSquared(x, y, yErr) { x1 ->
val chi2 = DSProcessor.chiSquaredExpression(x, y, yErr) { arg ->
//bind variables to autodiff context
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
a * arg.pow(2) + b * arg + c
}
//minimize the chi^2 in given starting point. Derivatives are not required, they are already included.
val result: OptimizationResult<Double> = chi2.minimize(a to 1.5, b to 0.9, c to 1.0)
val result = chi2.optimizeWith(
CMOptimizer,
mapOf(a to 1.5, b to 0.9, c to 1.0),
FunctionOptimizationTarget.MINIMIZE
)
//display a page with plot and numerical results
val page = Plotly.page {
@ -92,7 +98,7 @@ suspend fun main() {
scatter {
mode = ScatterMode.lines
x(x)
y(x.map { result.point[a]!! * it.pow(2) + result.point[b]!! * it + 1 })
y(x.map { result.resultPoint[a]!! * it.pow(2) + result.resultPoint[b]!! * it + 1 })
name = "fit"
}
}
@ -101,7 +107,7 @@ suspend fun main() {
+"Fit result: $result"
}
h3 {
+"Chi2/dof = ${result.value / (x.size - 3)}"
+"Chi2/dof = ${result.resultValue / (x.size - 3)}"
}
}

106
examples/src/main/kotlin/space/kscience/kmath/fit/qowFit.kt Normal file
View File

@ -0,0 +1,106 @@
/*
* 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.fit
import kotlinx.html.br
import kotlinx.html.h3
import space.kscience.kmath.commons.expressions.DSProcessor
import space.kscience.kmath.data.XYErrorColumnarData
import space.kscience.kmath.distributions.NormalDistribution
import space.kscience.kmath.expressions.Symbol
import space.kscience.kmath.expressions.binding
import space.kscience.kmath.expressions.symbol
import space.kscience.kmath.operations.asIterable
import space.kscience.kmath.operations.toList
import space.kscience.kmath.optimization.QowOptimizer
import space.kscience.kmath.optimization.chiSquaredOrNull
import space.kscience.kmath.optimization.fitWith
import space.kscience.kmath.optimization.resultPoint
import space.kscience.kmath.real.map
import space.kscience.kmath.real.step
import space.kscience.kmath.stat.RandomGenerator
import space.kscience.plotly.*
import space.kscience.plotly.models.ScatterMode
import kotlin.math.abs
import kotlin.math.pow
import kotlin.math.sqrt
// Forward declaration of symbols that will be used in expressions.
private val a by symbol
private val b by symbol
private val c by symbol
/**
* Least squares fie with auto-differentiation. Uses `kmath-commons` and `kmath-for-real` modules.
*/
suspend fun main() {
//A generator for a normally distributed values
val generator = NormalDistribution(0.0, 1.0)
//A chain/flow of random values with the given seed
val chain = generator.sample(RandomGenerator.default(112667))
//Create a uniformly distributed x values like numpy.arrange
val x = 1.0..100.0 step 1.0
//Perform an operation on each x value (much more effective, than numpy)
val y = x.map { it ->
val value = it.pow(2) + it + 1
value + chain.next() * sqrt(value)
}
// this will also work, but less effective:
// val y = x.pow(2)+ x + 1 + chain.nextDouble()
// create same errors for all xs
val yErr = y.map { sqrt(abs(it)) }
require(yErr.asIterable().all { it > 0 }) { "All errors must be strictly positive" }
val result = XYErrorColumnarData.of(x, y, yErr).fitWith(
QowOptimizer,
DSProcessor,
mapOf(a to 0.9, b to 1.2, c to 2.0)
) { arg ->
//bind variables to autodiff context
val a by binding
val b by binding
//Include default value for c if it is not provided as a parameter
val c = bindSymbolOrNull(c) ?: one
a * arg.pow(2) + b * arg + c
}
//display a page with plot and numerical results
val page = Plotly.page {
plot {
scatter {
mode = ScatterMode.markers
x(x)
y(y)
error_y {
array = yErr.toList()
}
name = "data"
}
scatter {
mode = ScatterMode.lines
x(x)
y(x.map { result.model(result.resultPoint + (Symbol.x to it)) })
name = "fit"
}
}
br()
h3 {
+"Fit result: ${result.resultPoint}"
}
h3 {
+"Chi2/dof = ${result.chiSquaredOrNull!! / (x.size - 3)}"
}
}
page.makeFile()
}

5
examples/src/main/kotlin/space/kscience/kmath/functions/integrate.kt
View File

@ -1,10 +1,11 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE 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
@ -15,7 +16,7 @@ fun main() {
val function: UnivariateFunction<Double> = { x -> 3 * x.pow(2) + 2 * x + 1 }
//get the result of the integration
val result = DoubleField.integrate(0.0..10.0, function = function)
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
View File

@ -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 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
View File

@ -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 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.map
import space.kscience.kmath.real.step
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()
}

15
examples/src/main/kotlin/space/kscience/kmath/functions/matrixIntegration.kt
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@ -1,19 +1,20 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE 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.nd.withNdAlgebra
import space.kscience.kmath.operations.algebra
import space.kscience.kmath.operations.invoke
fun main(): Unit = DoubleField {
nd(2, 2) {
fun main(): Unit = Double.algebra {
withNdAlgebra(2, 2) {
//Produce a diagonal StructureND
fun diagonal(v: Double) = produce { (i, j) ->
@ -21,10 +22,10 @@ fun main(): Unit = DoubleField {
}
//Define a function in a nd space
val function: (Double) -> StructureND<Double> = { x: Double -> 3 * number(x).pow(2) + 2 * diagonal(x) + 1 }
val function: (Double) -> StructureND<Double> = { x: Double -> 3 * x.pow(2) + 2 * diagonal(x) + 1 }
//get the result of the integration
val result = integrate(0.0..10.0, function = function)
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)

15
examples/src/main/kotlin/space/kscience/kmath/jafama/JafamaDemo.kt Normal file
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@ -0,0 +1,15 @@
/*
* 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 file.
*/
package space.kscience.kmath.jafama
import space.kscience.kmath.operations.invoke
fun main() {
val a = 2.0
val b = StrictJafamaDoubleField { exp(a) }
println(JafamaDoubleField { b + a })
println(StrictJafamaDoubleField { ln(b) })
}

34
examples/src/main/kotlin/space/kscience/kmath/linear/dotPerformance.kt Normal file
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@ -0,0 +1,34 @@
/*
* 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 file.
*/
package space.kscience.kmath.linear
import space.kscience.kmath.operations.algebra
import kotlin.random.Random
import kotlin.system.measureTimeMillis
fun main() {
val random = Random(12224)
val dim = 1000
//creating invertible matrix
val matrix1 = Double.algebra.linearSpace.buildMatrix(dim, dim) { i, j ->
if (i <= j) random.nextDouble() else 0.0
}
val matrix2 = Double.algebra.linearSpace.buildMatrix(dim, dim) { i, j ->
if (i <= j) random.nextDouble() else 0.0
}
val time = measureTimeMillis {
with(Double.algebra.linearSpace) {
repeat(10) {
matrix1 dot matrix2
}
}
}
println(time)
}

2
examples/src/main/kotlin/space/kscience/kmath/linear/gradient.kt
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@ -1,6 +1,6 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.linear

2
examples/src/main/kotlin/space/kscience/kmath/operations/BigIntDemo.kt
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@ -1,6 +1,6 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.operations

29
examples/src/main/kotlin/space/kscience/kmath/operations/ComplexDemo.kt
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@ -1,29 +0,0 @@
/*
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.operations
import space.kscience.kmath.complex.Complex
import space.kscience.kmath.complex.complex
import space.kscience.kmath.nd.AlgebraND
fun main() {
// 2d element
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(AlgebraND.complex(8)) {
val a = produce { (it) -> i * it - it.toDouble() }
val b = 3
val c = Complex(1.0, 1.0)
(a pow b) + c
}
println(result)
}

41
examples/src/main/kotlin/space/kscience/kmath/operations/complexDemo.kt Normal file
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@ -0,0 +1,41 @@
/*
* 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 file.
*/
package space.kscience.kmath.operations
import space.kscience.kmath.complex.Complex
import space.kscience.kmath.complex.algebra
import space.kscience.kmath.complex.bufferAlgebra
import space.kscience.kmath.complex.ndAlgebra
import space.kscience.kmath.nd.BufferND
import space.kscience.kmath.nd.StructureND
fun main() = Complex.algebra {
val complex = 2 + 2 * i
println(complex * 8 - 5 * i)
//flat buffer
val buffer = bufferAlgebra(8).run {
buffer { Complex(it, -it) }.map { Complex(it.im, it.re) }
}
println(buffer)
// 2d element
val element: BufferND<Complex> = ndAlgebra(2, 2).produce { (i, j) ->
Complex(i - j, i + j)
}
println(element)
// 1d element operation
val result: StructureND<Complex> = ndAlgebra(8).run {
val a = produce { (it) -> i * it - it.toDouble() }
val b = 3
val c = Complex(1.0, 1.0)
(a pow b) + c
}
println(result)
}

2
examples/src/main/kotlin/space/kscience/kmath/stat/DistributionBenchmark.kt
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@ -1,6 +1,6 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.stat

5
examples/src/main/kotlin/space/kscience/kmath/stat/DistributionDemo.kt
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@ -1,6 +1,6 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.stat
@ -10,9 +10,6 @@ import space.kscience.kmath.chains.Chain
import space.kscience.kmath.chains.collectWithState
import space.kscience.kmath.distributions.NormalDistribution
/**
* The state of distribution averager.
*/
private data class AveragingChainState(var num: Int = 0, var value: Double = 0.0)
/**

12
examples/src/main/kotlin/space/kscience/kmath/structures/ComplexND.kt
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@ -1,6 +1,6 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
@file:Suppress("unused")
@ -9,10 +9,10 @@ package space.kscience.kmath.structures
import space.kscience.kmath.complex.*
import space.kscience.kmath.linear.transpose
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.nd.ndAlgebra
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.invoke
import kotlin.system.measureTimeMillis
@ -20,8 +20,8 @@ fun main() {
val dim = 1000
val n = 1000
val realField = AlgebraND.real(dim, dim)
val complexField: ComplexFieldND = AlgebraND.complex(dim, dim)
val realField = DoubleField.ndAlgebra(dim, dim)
val complexField: ComplexFieldND = ComplexField.ndAlgebra(dim, dim)
val realTime = measureTimeMillis {
realField {
@ -49,7 +49,7 @@ fun main() {
fun complexExample() {
//Create a context for 2-d structure with complex values
ComplexField {
nd(4, 8) {
withNdAlgebra(4, 8) {
//a constant real-valued structure
val x = one * 2.5
operator fun Number.plus(other: Complex) = Complex(this.toDouble() + other.re, other.im)

16
examples/src/main/kotlin/space/kscience/kmath/structures/NDField.kt
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@ -1,6 +1,6 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.structures
@ -8,7 +8,9 @@ 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.nd.StructureND
import space.kscience.kmath.nd.autoNdAlgebra
import space.kscience.kmath.nd.ndAlgebra
import space.kscience.kmath.nd4j.Nd4jArrayField
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.invoke
@ -31,17 +33,17 @@ fun main() {
val n = 1000
// automatically build context most suited for given type.
val autoField = AlgebraND.auto(DoubleField, dim, dim)
// specialized nd-field for Double. It works as generic Double field as well
val realField = AlgebraND.real(dim, dim)
val autoField = DoubleField.autoNdAlgebra(dim, dim)
// specialized nd-field for Double. It works as generic Double field as well.
val realField = DoubleField.ndAlgebra(dim, dim)
//A generic boxing field. It should be used for objects, not primitives.
val boxingField = AlgebraND.field(DoubleField, Buffer.Companion::boxing, dim, dim)
val boxingField = DoubleField.ndAlgebra(Buffer.Companion::boxing, dim, dim)
// Nd4j specialized field.
val nd4jField = Nd4jArrayField.real(dim, dim)
//viktor field
val viktorField = ViktorNDField(dim, dim)
//parallel processing based on Java Streams
val parallelField = AlgebraND.realWithStream(dim, dim)
val parallelField = DoubleField.ndStreaming(dim, dim)
measureAndPrint("Boxing addition") {
boxingField {

4
examples/src/main/kotlin/space/kscience/kmath/structures/StreamDoubleFieldND.kt
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@ -1,6 +1,6 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.structures
@ -105,4 +105,4 @@ class StreamDoubleFieldND(override val shape: IntArray) : FieldND<Double, Double
override fun atanh(arg: StructureND<Double>): BufferND<Double> = arg.map { atanh(it) }
}
fun AlgebraND.Companion.realWithStream(vararg shape: Int): StreamDoubleFieldND = StreamDoubleFieldND(shape)
fun DoubleField.ndStreaming(vararg shape: Int): StreamDoubleFieldND = StreamDoubleFieldND(shape)

2
examples/src/main/kotlin/space/kscience/kmath/structures/StructureReadBenchmark.kt
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@ -1,6 +1,6 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.structures

2
examples/src/main/kotlin/space/kscience/kmath/structures/StructureWriteBenchmark.kt
View File

@ -1,6 +1,6 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.structures

22
examples/src/main/kotlin/space/kscience/kmath/structures/buffers.kt Normal file
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@ -0,0 +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.structures
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.buffer
import space.kscience.kmath.operations.bufferAlgebra
inline fun <reified R : Any> MutableBuffer.Companion.same(
n: Int,
value: R
): MutableBuffer<R> = auto(n) { value }
fun main() {
with(DoubleField.bufferAlgebra(5)) {
println(number(2.0) + buffer(1, 2, 3, 4, 5))
}
}

4
examples/src/main/kotlin/space/kscience/kmath/structures/typeSafeDimensions.kt
View File

@ -1,6 +1,6 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.structures
@ -19,7 +19,7 @@ private fun DMatrixContext<Double, *>.simple() {
}
private object D5 : Dimension {
override val dim: UInt = 5u
override val dim: Int = 5
}
private fun DMatrixContext<Double, *>.custom() {

46
examples/src/main/kotlin/space/kscience/kmath/tensors/DataSetNormalization.kt
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@ -1,46 +0,0 @@
/*
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.tensors
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
// Dataset normalization
fun main() {
// work in context with broadcast methods
BroadcastDoubleTensorAlgebra {
// 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)}")
}
}

97
examples/src/main/kotlin/space/kscience/kmath/tensors/LinearSystemSolvingWithLUP.kt
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@ -1,97 +0,0 @@
/*
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.tensors
import space.kscience.kmath.operations.invoke
import space.kscience.kmath.tensors.core.DoubleTensor
import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
// solving linear system with LUP decomposition
fun main () {
// work in context with linear operations
BroadcastDoubleTensorAlgebra {
// 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")
}
}

2
examples/src/main/kotlin/space/kscience/kmath/tensors/OLSWithSVD.kt
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@ -1,6 +1,6 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.tensors

102
examples/src/main/kotlin/space/kscience/kmath/tensors/PCA.kt
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@ -1,78 +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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE 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.tensorAlgebra
import space.kscience.kmath.tensors.core.withBroadcast
// simple PCA
fun main(){
fun main(): Unit = Double.tensorAlgebra.withBroadcast { // work in context with broadcast methods
val seed = 100500L
// work in context with broadcast methods
BroadcastDoubleTensorAlgebra {
// assume x is range from 0 until 10
val x = fromArray(
intArrayOf(10),
DoubleArray(10) { it.toDouble() }
)
// 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)
// 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")
println("x:\n$x")
println("y:\n$y")
// stack them into single dataset
val dataset = stack(listOf(x, y)).transpose()
// 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()
// normalize both x and y
val xMean = x.mean()
val yMean = y.mean()
val xStd = x.std()
val yStd = y.std()
val xStd = x.std()
val yStd = y.std()
val xScaled = (x - xMean) / xStd
val yScaled = (y - yMean) / yStd
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$mean")
// save means ans standard deviations for further recovery
val mean = fromArray(
intArrayOf(2),
doubleArrayOf(xMean, yMean)
)
println("Means:\n$mean")
val std = fromArray(
intArrayOf(2),
doubleArrayOf(xStd, yStd)
)
println("Standard deviations:\n$std")
val std = fromArray(
intArrayOf(2),
doubleArrayOf(xStd, yStd)
)
println("Standard deviations:\n$std")
// calculate the covariance matrix of scaled x and y
val covMatrix = cov(listOf(xScaled, yScaled))
println("Covariance matrix:\n$covMatrix")
// calculate the covariance matrix of scaled x and y
val covMatrix = cov(listOf(xScaled, yScaled))
println("Covariance matrix:\n$covMatrix")
// and find out eigenvector of it
val (_, evecs) = covMatrix.symEig()
val v = evecs[0]
println("Eigenvector:\n$v")
// and find out eigenvector of it
val (_, evecs) = covMatrix.symEig()
val v = evecs[0]
println("Eigenvector:\n$v")
// reduce dimension of dataset
val datasetReduced = v dot stack(listOf(xScaled, yScaled))
println("Reduced data:\n$datasetReduced")
// reduce dimension of dataset
val datasetReduced = v dot stack(listOf(xScaled, yScaled))
println("Reduced data:\n$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")
}
// 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")
}

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 file.
*/
package space.kscience.kmath.tensors
import space.kscience.kmath.tensors.core.tensorAlgebra
import space.kscience.kmath.tensors.core.withBroadcast
// Dataset normalization
fun main() = Double.tensorAlgebra.withBroadcast { // 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) // row 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
View File

@ -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 file.
*/
package space.kscience.kmath.tensors
import space.kscience.kmath.tensors.core.DoubleTensor
import space.kscience.kmath.tensors.core.tensorAlgebra
import space.kscience.kmath.tensors.core.withBroadcast
// solving linear system with LUP decomposition
fun main() = Double.tensorAlgebra.withBroadcast {// 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 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")
}

114
examples/src/main/kotlin/space/kscience/kmath/tensors/NeuralNetwork.kt → examples/src/main/kotlin/space/kscience/kmath/tensors/neuralNetwork.kt
View File

@ -1,6 +1,6 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.tensors
@ -25,7 +25,7 @@ interface Layer {
// activation layer
open class Activation(
val activation: (DoubleTensor) -> DoubleTensor,
val activationDer: (DoubleTensor) -> DoubleTensor
val activationDer: (DoubleTensor) -> DoubleTensor,
) : Layer {
override fun forward(input: DoubleTensor): DoubleTensor {
return activation(input)
@ -62,7 +62,7 @@ class Sigmoid : Activation(::sigmoid, ::sigmoidDer)
class Dense(
private val inputUnits: Int,
private val outputUnits: Int,
private val learningRate: Double = 0.1
private val learningRate: Double = 0.1,
) : Layer {
private val weights: DoubleTensor = DoubleTensorAlgebra {
@ -74,8 +74,8 @@ class Dense(
private val bias: DoubleTensor = DoubleTensorAlgebra { zeros(intArrayOf(outputUnits)) }
override fun forward(input: DoubleTensor): DoubleTensor {
return BroadcastDoubleTensorAlgebra { (input dot weights) + bias }
override fun forward(input: DoubleTensor): DoubleTensor = BroadcastDoubleTensorAlgebra {
(input dot weights) + bias
}
override fun backward(input: DoubleTensor, outputError: DoubleTensor): DoubleTensor = DoubleTensorAlgebra {
@ -116,7 +116,7 @@ class NeuralNetwork(private val layers: List<Layer>) {
onesForAnswers[intArrayOf(index, label)] = 1.0
}
val softmaxValue = yPred.exp() / yPred.exp().sum(dim = 1, keepDim = true)
val softmaxValue = yPred.exp() / yPred.exp().sum(dim = 1, keepDim = true)
(-onesForAnswers + softmaxValue) / (yPred.shape[0].toDouble())
}
@ -175,67 +175,65 @@ class NeuralNetwork(private val layers: List<Layer>) {
@OptIn(ExperimentalStdlibApi::class)
fun main() {
BroadcastDoubleTensorAlgebra {
val features = 5
val sampleSize = 250
val trainSize = 180
//val testSize = sampleSize - trainSize
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
// 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
)
x += fromArray(
intArrayOf(5),
doubleArrayOf(0.0, -1.0, -2.5, -3.0, 5.5) // row 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
}
// 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)
// split train ans test
val trainIndices = (0 until trainSize).toList().toIntArray()
val testIndices = (trainSize until sampleSize).toList().toIntArray()
// make prediction
val prediction = model.predict(xTest)
val xTrain = x.rowsByIndices(trainIndices)
val yTrain = y.rowsByIndices(trainIndices)
// process raw prediction via argMax
val predictionLabels = prediction.argMax(1, true)
// find out accuracy
val acc = accuracy(yTest, predictionLabels)
println("Test accuracy:$acc")
val xTest = x.rowsByIndices(testIndices)
val yTest = y.rowsByIndices(testIndices)
// build model
val layers = buildList {
add(Dense(features, 64))
add(ReLU())
add(Dense(64, 16))
add(ReLU())
add(Dense(16, 2))
add(Sigmoid())
}
val model = NeuralNetwork(layers)
// fit it with train data
model.fit(xTrain, yTrain, batchSize = 20, epochs = 10)
// make prediction
val prediction = model.predict(xTest)
// process raw prediction via argMax
val predictionLabels = prediction.argMax(1, true)
// find out accuracy
val acc = accuracy(yTest, predictionLabels)
println("Test accuracy:$acc")
}

9
gradle.properties
View File

@ -4,10 +4,13 @@
#
kotlin.code.style=official
kotlin.mpp.enableGranularSourceSetsMetadata=true
kotlin.mpp.stability.nowarn=true
kotlin.native.enableDependencyPropagation=false
kotlin.parallel.tasks.in.project=true
#kotlin.mpp.enableGranularSourceSetsMetadata=true
#kotlin.native.enableDependencyPropagation=false
kotlin.jupyter.add.scanner=false
org.gradle.configureondemand=true
org.gradle.jvmargs=-XX:MaxMetaspaceSize=2G
org.gradle.parallel=true

BIN
gradle/wrapper/gradle-wrapper.jar vendored
View File

Binary file not shown.

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

@ -1,5 +1,5 @@
distributionBase=GRADLE_USER_HOME
distributionPath=wrapper/dists
distributionUrl=https\://services.gradle.org/distributions/gradle-7.0-bin.zip
distributionUrl=https\://services.gradle.org/distributions/gradle-7.1.1-bin.zip
zipStoreBase=GRADLE_USER_HOME
zipStorePath=wrapper/dists

2
gradlew vendored
View File

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

20
kmath-ast/README.md
View File

@ -10,7 +10,7 @@ Performance and visualization extensions to MST API.
## Artifact:
The Maven coordinates of this project are `space.kscience:kmath-ast:0.3.0-dev-8`.
The Maven coordinates of this project are `space.kscience:kmath-ast:0.3.0-dev-14`.
**Gradle:**
```gradle
@ -20,7 +20,7 @@ repositories {
}
dependencies {
implementation 'space.kscience:kmath-ast:0.3.0-dev-8'
implementation 'space.kscience:kmath-ast:0.3.0-dev-14'
}
```
**Gradle Kotlin DSL:**
@ -31,7 +31,7 @@ repositories {
}
dependencies {
implementation("space.kscience:kmath-ast:0.3.0-dev-8")
implementation("space.kscience:kmath-ast:0.3.0-dev-14")
}
```
@ -45,11 +45,12 @@ special implementation of `Expression<T>` with implemented `invoke` function.
For example, the following builder:
```kotlin
import space.kscience.kmath.expressions.Symbol.Companion.x
import space.kscience.kmath.expressions.*
import space.kscience.kmath.operations.*
import space.kscience.kmath.asm.*
MstField { bindSymbol("x") + 2 }.compileToExpression(DoubleField)
MstField { x + 2 }.compileToExpression(DoubleField)
```
... leads to generation of bytecode, which can be decompiled to the following Java class:
@ -89,11 +90,12 @@ public final class AsmCompiledExpression_45045_0 implements Expression<Double> {
A similar feature is also available on JS.
```kotlin
import space.kscience.kmath.expressions.Symbol.Companion.x
import space.kscience.kmath.expressions.*
import space.kscience.kmath.operations.*
import space.kscience.kmath.estree.*
MstField { bindSymbol("x") + 2 }.compileToExpression(DoubleField)
MstField { x + 2 }.compileToExpression(DoubleField)
```
The code above returns expression implemented with such a JS function:
@ -104,15 +106,16 @@ var executable = function (constants, arguments) {
};
```
JS also supports very experimental expression optimization with [WebAssembly](https://webassembly.org/) IR generation.
JS also supports 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 { bindSymbol("x") + 2 }.compileToExpression(DoubleField)
MstField { x + 2 }.compileToExpression(DoubleField)
```
An example of emitted Wasm IR in the form of WAT:
@ -158,7 +161,10 @@ public fun main() {
Result LaTeX:
<div style="background-color:white;">
![](https://latex.codecogs.com/gif.latex?%5Coperatorname{exp}%5C,%5Cleft(%5Csqrt{x}%5Cright)-%5Cfrac{%5Cfrac{%5Coperatorname{arcsin}%5C,%5Cleft(2%5C,x%5Cright)}{2%5Ctimes10^{10}%2Bx^{3}}}{12}+x^{2/3})
</div>
Result MathML (can be used with MathJax or other renderers):

22
kmath-ast/build.gradle.kts
View File

@ -37,17 +37,15 @@ kotlin.sourceSets {
jsMain {
dependencies {
implementation(npm("astring", "1.7.4"))
implementation(npm("binaryen", "100.0"))
implementation(npm("js-base64", "3.6.0"))
implementation(npm("webassembly", "0.11.0"))
implementation(npm("astring", "1.7.5"))
implementation(npm("binaryen", "101.0.0"))
implementation(npm("js-base64", "3.6.1"))
}
}
jvmMain {
dependencies {
implementation("org.ow2.asm:asm:9.1")
implementation("org.ow2.asm:asm-commons:9.1")
implementation("org.ow2.asm:asm-commons:9.2")
}
}
}
@ -63,25 +61,21 @@ readme {
feature(
id = "expression-language",
description = "Expression language and its parser",
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/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/space/kscience/kmath/estree/estree.kt"
)
) { "Dynamic MST to JS compiler" }
feature(
id = "rendering",
description = "Extendable MST rendering",
ref = "src/commonMain/kotlin/space/kscience/kmath/ast/rendering/MathRenderer.kt"
)
) { "Extendable MST rendering" }
}

14
kmath-ast/docs/README-TEMPLATE.md
View File

@ -16,11 +16,12 @@ special implementation of `Expression<T>` with implemented `invoke` function.
For example, the following builder:
```kotlin
import space.kscience.kmath.expressions.Symbol.Companion.x
import space.kscience.kmath.expressions.*
import space.kscience.kmath.operations.*
import space.kscience.kmath.asm.*
MstField { bindSymbol("x") + 2 }.compileToExpression(DoubleField)
MstField { x + 2 }.compileToExpression(DoubleField)
```
... leads to generation of bytecode, which can be decompiled to the following Java class:
@ -60,11 +61,12 @@ public final class AsmCompiledExpression_45045_0 implements Expression<Double> {
A similar feature is also available on JS.
```kotlin
import space.kscience.kmath.expressions.Symbol.Companion.x
import space.kscience.kmath.expressions.*
import space.kscience.kmath.operations.*
import space.kscience.kmath.estree.*
MstField { bindSymbol("x") + 2 }.compileToExpression(DoubleField)
MstField { x + 2 }.compileToExpression(DoubleField)
```
The code above returns expression implemented with such a JS function:
@ -75,15 +77,16 @@ var executable = function (constants, arguments) {
};
```
JS also supports very experimental expression optimization with [WebAssembly](https://webassembly.org/) IR generation.
JS also supports 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 { bindSymbol("x") + 2 }.compileToExpression(DoubleField)
MstField { x + 2 }.compileToExpression(DoubleField)
```
An example of emitted Wasm IR in the form of WAT:
@ -129,7 +132,10 @@ public fun main() {
Result LaTeX:
<div style="background-color:white;">
![](https://latex.codecogs.com/gif.latex?%5Coperatorname{exp}%5C,%5Cleft(%5Csqrt{x}%5Cright)-%5Cfrac{%5Cfrac{%5Coperatorname{arcsin}%5C,%5Cleft(2%5C,x%5Cright)}{2%5Ctimes10^{10}%2Bx^{3}}}{12}+x^{2/3})
</div>
Result MathML (can be used with MathJax or other renderers):

5
kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/parser.kt
View File

@ -1,6 +1,6 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.ast
@ -17,6 +17,7 @@ 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.Symbol
import space.kscience.kmath.operations.FieldOperations
import space.kscience.kmath.operations.GroupOperations
import space.kscience.kmath.operations.PowerOperations
@ -42,7 +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 { Symbol(text) }
private val unaryFunction: Parser<MST> by (id and -lpar and parser(ArithmeticsEvaluator::subSumChain) and -rpar)
.map { (id, term) -> MST.Unary(id.text, term) }

4
kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/LatexSyntaxRenderer.kt
View File

@ -1,6 +1,6 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.ast.rendering
@ -27,7 +27,7 @@ import space.kscience.kmath.misc.UnstableKMathAPI
*/
@UnstableKMathAPI
public object LatexSyntaxRenderer : SyntaxRenderer {
public override fun render(node: MathSyntax, output: Appendable): Unit = output.run {
override fun render(node: MathSyntax, output: Appendable): Unit = output.run {
fun render(syntax: MathSyntax) = render(syntax, output)
when (node) {

4
kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/MathMLSyntaxRenderer.kt
View File

@ -1,6 +1,6 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.ast.rendering
@ -16,7 +16,7 @@ import space.kscience.kmath.misc.UnstableKMathAPI
*/
@UnstableKMathAPI
public object MathMLSyntaxRenderer : SyntaxRenderer {
public override fun render(node: MathSyntax, output: Appendable) {
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>")

12
kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/MathRenderer.kt
View File

@ -1,6 +1,6 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.ast.rendering
@ -29,7 +29,7 @@ public fun interface MathRenderer {
*/
@UnstableKMathAPI
public open class FeaturedMathRenderer(public val features: List<RenderFeature>) : MathRenderer {
public override fun render(mst: MST): MathSyntax {
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.")
}
@ -54,9 +54,9 @@ public open class FeaturedMathRenderer(public val features: List<RenderFeature>)
@UnstableKMathAPI
public open class FeaturedMathRendererWithPostProcess(
features: List<RenderFeature>,
public val stages: List<PostProcessStage>,
public val stages: List<PostProcessPhase>,
) : FeaturedMathRenderer(features) {
public override fun render(mst: MST): MathSyntax {
override fun render(mst: MST): MathSyntax {
val res = super.render(mst)
for (stage in stages) stage.perform(res)
return res
@ -65,7 +65,7 @@ public open class FeaturedMathRendererWithPostProcess(
/**
* Logical unit of [MathSyntax] post-processing.
*/
public fun interface PostProcessStage {
public fun interface PostProcessPhase {
/**
* Performs the specified action over [MathSyntax].
*/
@ -102,7 +102,7 @@ public open class FeaturedMathRendererWithPostProcess(
// Printing terminal nodes as string
PrintNumeric,
PrintSymbolic,
PrintSymbol,
),
listOf(
BetterExponent,

74
kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/MathSyntax.kt
View File

@ -1,6 +1,6 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.ast.rendering
@ -8,7 +8,7 @@ package space.kscience.kmath.ast.rendering
import space.kscience.kmath.misc.UnstableKMathAPI
/**
* Mathematical typography syntax node.
* Syntax node for mathematical typography.
*
* @author Iaroslav Postovalov
*/
@ -150,9 +150,9 @@ public data class OperandSyntax(
*/
@UnstableKMathAPI
public data class UnaryOperatorSyntax(
public override val operation: String,
override val operation: String,
public var prefix: MathSyntax,
public override val operand: OperandSyntax,
override val operand: OperandSyntax,
) : UnarySyntax() {
init {
operand.parent = this
@ -166,8 +166,8 @@ public data class UnaryOperatorSyntax(
*/
@UnstableKMathAPI
public data class UnaryPlusSyntax(
public override val operation: String,
public override val operand: OperandSyntax,
override val operation: String,
override val operand: OperandSyntax,
) : UnarySyntax() {
init {
operand.parent = this
@ -181,8 +181,8 @@ public data class UnaryPlusSyntax(
*/
@UnstableKMathAPI
public data class UnaryMinusSyntax(
public override val operation: String,
public override val operand: OperandSyntax,
override val operation: String,
override val operand: OperandSyntax,
) : UnarySyntax() {
init {
operand.parent = this
@ -197,8 +197,8 @@ public data class UnaryMinusSyntax(
*/
@UnstableKMathAPI
public data class RadicalSyntax(
public override val operation: String,
public override val operand: MathSyntax,
override val operation: String,
override val operand: MathSyntax,
) : UnarySyntax() {
init {
operand.parent = this
@ -215,8 +215,8 @@ public data class RadicalSyntax(
*/
@UnstableKMathAPI
public data class ExponentSyntax(
public override val operation: String,
public override val operand: OperandSyntax,
override val operation: String,
override val operand: OperandSyntax,
public var useOperatorForm: Boolean,
) : UnarySyntax() {
init {
@ -233,9 +233,9 @@ public data class ExponentSyntax(
*/
@UnstableKMathAPI
public data class SuperscriptSyntax(
public override val operation: String,
public override val left: MathSyntax,
public override val right: MathSyntax,
override val operation: String,
override val left: MathSyntax,
override val right: MathSyntax,
) : BinarySyntax() {
init {
left.parent = this
@ -252,9 +252,9 @@ public data class SuperscriptSyntax(
*/
@UnstableKMathAPI
public data class SubscriptSyntax(
public override val operation: String,
public override val left: MathSyntax,
public override val right: MathSyntax,
override val operation: String,
override val left: MathSyntax,
override val right: MathSyntax,
) : BinarySyntax() {
init {
left.parent = this
@ -270,10 +270,10 @@ public data class SubscriptSyntax(
*/
@UnstableKMathAPI
public data class BinaryOperatorSyntax(
public override val operation: String,
override val operation: String,
public var prefix: MathSyntax,
public override val left: MathSyntax,
public override val right: MathSyntax,
override val left: MathSyntax,
override val right: MathSyntax,
) : BinarySyntax() {
init {
left.parent = this
@ -290,9 +290,9 @@ public data class BinaryOperatorSyntax(
*/
@UnstableKMathAPI
public data class BinaryPlusSyntax(
public override val operation: String,
public override val left: OperandSyntax,
public override val right: OperandSyntax,
override val operation: String,
override val left: OperandSyntax,
override val right: OperandSyntax,
) : BinarySyntax() {
init {
left.parent = this
@ -301,7 +301,7 @@ public data class BinaryPlusSyntax(
}
/**
* Represents binary, infix subtraction (*42 - 42*).
* Represents binary, infix subtraction (*42 &minus; 42*).
*
* @param left The minuend.
* @param right The subtrahend.
@ -309,9 +309,9 @@ public data class BinaryPlusSyntax(
*/
@UnstableKMathAPI
public data class BinaryMinusSyntax(
public override val operation: String,
public override val left: OperandSyntax,
public override val right: OperandSyntax,
override val operation: String,
override val left: OperandSyntax,
override val right: OperandSyntax,
) : BinarySyntax() {
init {
left.parent = this
@ -329,9 +329,9 @@ public data class BinaryMinusSyntax(
*/
@UnstableKMathAPI
public data class FractionSyntax(
public override val operation: String,
public override val left: OperandSyntax,
public override val right: OperandSyntax,
override val operation: String,
override val left: OperandSyntax,
override val right: OperandSyntax,
public var infix: Boolean,
) : BinarySyntax() {
init {
@ -349,9 +349,9 @@ public data class FractionSyntax(
*/
@UnstableKMathAPI
public data class RadicalWithIndexSyntax(
public override val operation: String,
public override val left: MathSyntax,
public override val right: MathSyntax,
override val operation: String,
override val left: MathSyntax,
override val right: MathSyntax,
) : BinarySyntax() {
init {
left.parent = this
@ -369,9 +369,9 @@ public data class RadicalWithIndexSyntax(
*/
@UnstableKMathAPI
public data class MultiplicationSyntax(
public override val operation: String,
public override val left: OperandSyntax,
public override val right: OperandSyntax,
override val operation: String,
override val left: OperandSyntax,
override val right: OperandSyntax,
public var times: Boolean,
) : BinarySyntax() {
init {

4
kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/SyntaxRenderer.kt
View File

@ -1,6 +1,6 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.ast.rendering
@ -9,7 +9,7 @@ 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.
* involve traversal of MathSyntax with handling each subtype.
*
* @author Iaroslav Postovalov
*/

59
kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/features.kt
View File

@ -1,27 +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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE 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 [MST.Symbolic] as a [SymbolSyntax] containing the [MST.Symbolic.value] of it.
* Prints any [Symbol] as a [SymbolSyntax] containing the [Symbol.identity] of it.
*
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public object PrintSymbolic : RenderFeature {
public override fun render(renderer: FeaturedMathRenderer, node: MST): SymbolSyntax? =
if (node !is MST.Symbolic) null
else
SymbolSyntax(string = node.value)
public val PrintSymbol: RenderFeature = RenderFeature { _, node ->
if (node !is Symbol) null
else SymbolSyntax(string = node.identity)
}
/**
@ -30,8 +29,8 @@ public object PrintSymbolic : RenderFeature {
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public object PrintNumeric : RenderFeature {
public override fun render(renderer: FeaturedMathRenderer, node: MST): NumberSyntax? = if (node !is MST.Numeric)
public val PrintNumeric: RenderFeature = RenderFeature { _, node ->
if (node !is MST.Numeric)
null
else
NumberSyntax(string = node.value.toString())
@ -50,7 +49,7 @@ else
NumberSyntax(string = s)
/**
* Special printing for numeric types which are printed in form of
* Special printing for numeric types that are printed in form of
* *('-'? (DIGIT+ ('.' DIGIT+)? ('E' '-'? DIGIT+)? | 'Infinity')) | 'NaN'*.
*
* @property types The suitable types.
@ -58,7 +57,7 @@ else
*/
@UnstableKMathAPI
public class PrettyPrintFloats(public val types: Set<KClass<out Number>>) : RenderFeature {
public override fun render(renderer: FeaturedMathRenderer, node: MST): MathSyntax? {
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) {
@ -111,14 +110,14 @@ public class PrettyPrintFloats(public val types: Set<KClass<out Number>>) : Rend
}
/**
* Special printing for numeric types which are printed in form of *'-'? DIGIT+*.
* Special printing for numeric types that 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? =
override fun render(renderer: FeaturedMathRenderer, node: MST): MathSyntax? =
if (node !is MST.Numeric || node.value::class !in types)
null
else
@ -141,8 +140,8 @@ public class PrettyPrintIntegers(public val types: Set<KClass<out Number>>) : Re
*/
@UnstableKMathAPI
public class PrettyPrintPi(public val symbols: Set<String>) : RenderFeature {
public override fun render(renderer: FeaturedMathRenderer, node: MST): SpecialSymbolSyntax? =
if (node !is MST.Symbolic || node.value !in symbols)
override fun render(renderer: FeaturedMathRenderer, node: MST): MathSyntax? =
if (node !is Symbol || node.identity !in symbols)
null
else
SpecialSymbolSyntax(kind = SpecialSymbolSyntax.Kind.SMALL_PI)
@ -156,7 +155,7 @@ public class PrettyPrintPi(public val symbols: Set<String>) : RenderFeature {
}
/**
* Abstract printing of unary operations which discards [MST] if their operation is not in [operations] or its type is
* Abstract printing of unary operations that 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.
@ -177,7 +176,7 @@ public abstract class Unary(public val operations: Collection<String>?) : Render
}
/**
* Abstract printing of unary operations which discards [MST] if their operation is not in [operations] or its type is
* Abstract printing of unary operations that 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.
@ -203,7 +202,7 @@ public abstract class Binary(public val operations: Collection<String>?) : Rende
*/
@UnstableKMathAPI
public class BinaryPlus(operations: Collection<String>?) : Binary(operations) {
public override fun renderBinary(parent: FeaturedMathRenderer, node: MST.Binary): BinaryPlusSyntax =
override fun renderBinary(parent: FeaturedMathRenderer, node: MST.Binary): MathSyntax =
BinaryPlusSyntax(
operation = node.operation,
left = OperandSyntax(parent.render(node.left), true),
@ -225,7 +224,7 @@ public class BinaryPlus(operations: Collection<String>?) : Binary(operations) {
*/
@UnstableKMathAPI
public class BinaryMinus(operations: Collection<String>?) : Binary(operations) {
public override fun renderBinary(parent: FeaturedMathRenderer, node: MST.Binary): BinaryMinusSyntax =
override fun renderBinary(parent: FeaturedMathRenderer, node: MST.Binary): MathSyntax =
BinaryMinusSyntax(
operation = node.operation,
left = OperandSyntax(operand = parent.render(node.left), parentheses = true),
@ -247,7 +246,7 @@ public class BinaryMinus(operations: Collection<String>?) : Binary(operations) {
*/
@UnstableKMathAPI
public class UnaryPlus(operations: Collection<String>?) : Unary(operations) {
public override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): UnaryPlusSyntax = UnaryPlusSyntax(
override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): MathSyntax = UnaryPlusSyntax(
operation = node.operation,
operand = OperandSyntax(operand = parent.render(node.value), parentheses = true),
)
@ -267,7 +266,7 @@ public class UnaryPlus(operations: Collection<String>?) : Unary(operations) {
*/
@UnstableKMathAPI
public class UnaryMinus(operations: Collection<String>?) : Unary(operations) {
public override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): UnaryMinusSyntax = UnaryMinusSyntax(
override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): MathSyntax = UnaryMinusSyntax(
operation = node.operation,
operand = OperandSyntax(operand = parent.render(node.value), parentheses = true),
)
@ -287,7 +286,7 @@ public class UnaryMinus(operations: Collection<String>?) : Unary(operations) {
*/
@UnstableKMathAPI
public class Fraction(operations: Collection<String>?) : Binary(operations) {
public override fun renderBinary(parent: FeaturedMathRenderer, node: MST.Binary): FractionSyntax = FractionSyntax(
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),
@ -309,7 +308,7 @@ public class Fraction(operations: Collection<String>?) : Binary(operations) {
*/
@UnstableKMathAPI
public class BinaryOperator(operations: Collection<String>?) : Binary(operations) {
public override fun renderBinary(parent: FeaturedMathRenderer, node: MST.Binary): BinaryOperatorSyntax =
override fun renderBinary(parent: FeaturedMathRenderer, node: MST.Binary): MathSyntax =
BinaryOperatorSyntax(
operation = node.operation,
prefix = OperatorNameSyntax(name = node.operation),
@ -332,7 +331,7 @@ public class BinaryOperator(operations: Collection<String>?) : Binary(operations
*/
@UnstableKMathAPI
public class UnaryOperator(operations: Collection<String>?) : Unary(operations) {
public override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): UnaryOperatorSyntax =
override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): MathSyntax =
UnaryOperatorSyntax(
operation = node.operation,
prefix = OperatorNameSyntax(node.operation),
@ -354,7 +353,7 @@ public class UnaryOperator(operations: Collection<String>?) : Unary(operations)
*/
@UnstableKMathAPI
public class Power(operations: Collection<String>?) : Binary(operations) {
public override fun renderBinary(parent: FeaturedMathRenderer, node: MST.Binary): SuperscriptSyntax =
override fun renderBinary(parent: FeaturedMathRenderer, node: MST.Binary): MathSyntax =
SuperscriptSyntax(
operation = node.operation,
left = OperandSyntax(parent.render(node.left), true),
@ -374,7 +373,7 @@ public class Power(operations: Collection<String>?) : Binary(operations) {
*/
@UnstableKMathAPI
public class SquareRoot(operations: Collection<String>?) : Unary(operations) {
public override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): RadicalSyntax =
override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): MathSyntax =
RadicalSyntax(operation = node.operation, operand = parent.render(node.value))
public companion object {
@ -392,7 +391,7 @@ public class SquareRoot(operations: Collection<String>?) : Unary(operations) {
*/
@UnstableKMathAPI
public class Exponent(operations: Collection<String>?) : Unary(operations) {
public override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): ExponentSyntax = ExponentSyntax(
override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): MathSyntax = ExponentSyntax(
operation = node.operation,
operand = OperandSyntax(operand = parent.render(node.value), parentheses = true),
useOperatorForm = true,
@ -413,7 +412,7 @@ public class Exponent(operations: Collection<String>?) : Unary(operations) {
*/
@UnstableKMathAPI
public class Multiplication(operations: Collection<String>?) : Binary(operations) {
public override fun renderBinary(parent: FeaturedMathRenderer, node: MST.Binary): MultiplicationSyntax =
override fun renderBinary(parent: FeaturedMathRenderer, node: MST.Binary): MathSyntax =
MultiplicationSyntax(
operation = node.operation,
left = OperandSyntax(operand = parent.render(node.left), parentheses = true),
@ -436,7 +435,7 @@ public class Multiplication(operations: Collection<String>?) : Binary(operations
*/
@UnstableKMathAPI
public class InverseTrigonometricOperations(operations: Collection<String>?) : Unary(operations) {
public override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): UnaryOperatorSyntax =
override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): MathSyntax =
UnaryOperatorSyntax(
operation = node.operation,
prefix = OperatorNameSyntax(name = node.operation.replaceFirst("a", "arc")),
@ -463,7 +462,7 @@ public class InverseTrigonometricOperations(operations: Collection<String>?) : U
*/
@UnstableKMathAPI
public class InverseHyperbolicOperations(operations: Collection<String>?) : Unary(operations) {
public override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): UnaryOperatorSyntax =
override fun renderUnary(parent: FeaturedMathRenderer, node: MST.Unary): MathSyntax =
UnaryOperatorSyntax(
operation = node.operation,
prefix = OperatorNameSyntax(name = node.operation.replaceFirst("a", "ar")),

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

@ -1,6 +1,6 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.ast.rendering

93
kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/stages.kt → kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/phases.kt
View File

@ -1,10 +1,11 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE 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
@ -17,8 +18,8 @@ import space.kscience.kmath.operations.RingOperations
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public object BetterMultiplication : FeaturedMathRendererWithPostProcess.PostProcessStage {
public override fun perform(node: MathSyntax): Unit = when (node) {
public val BetterMultiplication: PostProcessPhase = PostProcessPhase { node ->
fun perform(node: MathSyntax): Unit = when (node) {
is NumberSyntax -> Unit
is SymbolSyntax -> Unit
is OperatorNameSyntax -> Unit
@ -81,6 +82,8 @@ public object BetterMultiplication : FeaturedMathRendererWithPostProcess.PostPro
perform(node.right)
}
}
perform(node)
}
/**
@ -89,68 +92,68 @@ public object BetterMultiplication : FeaturedMathRendererWithPostProcess.PostPro
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public object BetterFraction : FeaturedMathRendererWithPostProcess.PostProcessStage {
private fun perform0(node: MathSyntax, infix: Boolean = false): Unit = when (node) {
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 -> perform0(node.operand, infix)
is OperandSyntax -> perform(node.operand, infix)
is UnaryOperatorSyntax -> {
perform0(node.prefix, infix)
perform0(node.operand, infix)
perform(node.prefix, infix)
perform(node.operand, infix)
}
is UnaryPlusSyntax -> perform0(node.operand, infix)
is UnaryMinusSyntax -> perform0(node.operand, infix)
is RadicalSyntax -> perform0(node.operand, infix)
is ExponentSyntax -> perform0(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 -> {
perform0(node.left, true)
perform0(node.right, true)
perform(node.left, true)
perform(node.right, true)
}
is SubscriptSyntax -> {
perform0(node.left, true)
perform0(node.right, true)
perform(node.left, true)
perform(node.right, true)
}
is BinaryOperatorSyntax -> {
perform0(node.prefix, infix)
perform0(node.left, infix)
perform0(node.right, infix)
perform(node.prefix, infix)
perform(node.left, infix)
perform(node.right, infix)
}
is BinaryPlusSyntax -> {
perform0(node.left, infix)
perform0(node.right, infix)
perform(node.left, infix)
perform(node.right, infix)
}
is BinaryMinusSyntax -> {
perform0(node.left, infix)
perform0(node.right, infix)
perform(node.left, infix)
perform(node.right, infix)
}
is FractionSyntax -> {
node.infix = infix
perform0(node.left, infix)
perform0(node.right, infix)
perform(node.left, infix)
perform(node.right, infix)
}
is RadicalWithIndexSyntax -> {
perform0(node.left, true)
perform0(node.right, true)
perform(node.left, true)
perform(node.right, true)
}
is MultiplicationSyntax -> {
perform0(node.left, infix)
perform0(node.right, infix)
perform(node.left, infix)
perform(node.right, infix)
}
}
public override fun perform(node: MathSyntax): Unit = perform0(node)
perform(node)
}
/**
@ -160,39 +163,37 @@ public object BetterFraction : FeaturedMathRendererWithPostProcess.PostProcessSt
* @author Iaroslav Postovalov
*/
@UnstableKMathAPI
public object BetterExponent : FeaturedMathRendererWithPostProcess.PostProcessStage {
private fun perform0(node: MathSyntax): Boolean {
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 -> perform0(node.operand)
is UnaryOperatorSyntax -> perform0(node.prefix) || perform0(node.operand)
is UnaryPlusSyntax -> perform0(node.operand)
is UnaryMinusSyntax -> perform0(node.operand)
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 = perform0(node.operand)
val r = perform(node.operand)
node.useOperatorForm = r
r
}
is SuperscriptSyntax -> true
is SubscriptSyntax -> true
is BinaryOperatorSyntax -> perform0(node.prefix) || perform0(node.left) || perform0(node.right)
is BinaryPlusSyntax -> perform0(node.left) || perform0(node.right)
is BinaryMinusSyntax -> perform0(node.left) || perform0(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 -> true
is RadicalWithIndexSyntax -> true
is MultiplicationSyntax -> perform0(node.left) || perform0(node.right)
is MultiplicationSyntax -> perform(node.left) || perform(node.right)
}
}
public override fun perform(node: MathSyntax) {
perform0(node)
}
perform(node)
}
/**
@ -203,8 +204,8 @@ public object BetterExponent : FeaturedMathRendererWithPostProcess.PostProcessSt
*/
@UnstableKMathAPI
public class SimplifyParentheses(public val precedenceFunction: (MathSyntax) -> Int) :
FeaturedMathRendererWithPostProcess.PostProcessStage {
public override fun perform(node: MathSyntax): Unit = when (node) {
PostProcessPhase {
override fun perform(node: MathSyntax): Unit = when (node) {
is NumberSyntax -> Unit
is SymbolSyntax -> Unit
is OperatorNameSyntax -> Unit

9
kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/TestCompilerConsistencyWithInterpreter.kt
View File

@ -1,17 +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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE 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.misc.Symbol.Companion.x
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
@ -22,7 +21,7 @@ internal class TestCompilerConsistencyWithInterpreter {
val mst = MstRing {
binaryOperationFunction("+")(
unaryOperationFunction("+")(
(bindSymbol(x) - (2.toByte() + (scale(
(x - (2.toByte() + (scale(
add(number(1), number(1)),
2.0,
) + 1.toByte()))) * 3.0 - 1.toByte()
@ -42,7 +41,7 @@ internal class TestCompilerConsistencyWithInterpreter {
fun doubleField() = runCompilerTest {
val mst = MstField {
+(3 - 2 + 2 * number(1) + 1.0) + binaryOperationFunction("+")(
(3.0 - (bindSymbol(x) + (scale(add(number(1.0), number(1.0)), 2.0) + 1.0))) * 3 - 1.0
(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

21
kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/TestCompilerOperations.kt
View File

@ -1,15 +1,14 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE 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.misc.Symbol.Companion.x
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.bindSymbol
import space.kscience.kmath.operations.invoke
import kotlin.test.Test
import kotlin.test.assertEquals
@ -17,49 +16,49 @@ import kotlin.test.assertEquals
internal class TestCompilerOperations {
@Test
fun testUnaryPlus() = runCompilerTest {
val expr = MstExtendedField { +bindSymbol(x) }.compileToExpression(DoubleField)
val expr = MstExtendedField { +x }.compileToExpression(DoubleField)
assertEquals(2.0, expr(x to 2.0))
}
@Test
fun testUnaryMinus() = runCompilerTest {
val expr = MstExtendedField { -bindSymbol(x) }.compileToExpression(DoubleField)
val expr = MstExtendedField { -x }.compileToExpression(DoubleField)
assertEquals(-2.0, expr(x to 2.0))
}
@Test
fun testAdd() = runCompilerTest {
val expr = MstExtendedField { bindSymbol(x) + bindSymbol(x) }.compileToExpression(DoubleField)
val expr = MstExtendedField { x + x }.compileToExpression(DoubleField)
assertEquals(4.0, expr(x to 2.0))
}
@Test
fun testSine() = runCompilerTest {
val expr = MstExtendedField { sin(bindSymbol(x)) }.compileToExpression(DoubleField)
val expr = MstExtendedField { sin(x) }.compileToExpression(DoubleField)
assertEquals(0.0, expr(x to 0.0))
}
@Test
fun testCosine() = runCompilerTest {
val expr = MstExtendedField { cos(bindSymbol(x)) }.compileToExpression(DoubleField)
val expr = MstExtendedField { cos(x) }.compileToExpression(DoubleField)
assertEquals(1.0, expr(x to 0.0))
}
@Test
fun testSubtract() = runCompilerTest {
val expr = MstExtendedField { bindSymbol(x) - bindSymbol(x) }.compileToExpression(DoubleField)
val expr = MstExtendedField { x - x }.compileToExpression(DoubleField)
assertEquals(0.0, expr(x to 2.0))
}
@Test
fun testDivide() = runCompilerTest {
val expr = MstExtendedField { bindSymbol(x) / bindSymbol(x) }.compileToExpression(DoubleField)
val expr = MstExtendedField { x / x }.compileToExpression(DoubleField)
assertEquals(1.0, expr(x to 2.0))
}
@Test
fun testPower() = runCompilerTest {
val expr = MstExtendedField { bindSymbol(x) pow 2 }.compileToExpression(DoubleField)
val expr = MstExtendedField { x pow 2 }.compileToExpression(DoubleField)
assertEquals(4.0, expr(x to 2.0))
}
}

9
kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/TestCompilerVariables.kt
View File

@ -1,15 +1,14 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE 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.misc.Symbol.Companion.x
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
@ -18,13 +17,13 @@ import kotlin.test.assertFailsWith
internal class TestCompilerVariables {
@Test
fun testVariable() = runCompilerTest {
val expr = MstRing { bindSymbol(x) }.compileToExpression(IntRing)
val expr = MstRing { x }.compileToExpression(IntRing)
assertEquals(1, expr(x to 1))
}
@Test
fun testUndefinedVariableFails() = runCompilerTest {
val expr = MstRing { bindSymbol(x) }.compileToExpression(IntRing)
val expr = MstRing { x }.compileToExpression(IntRing)
assertFailsWith<NoSuchElementException> { expr() }
}
}

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

@ -1,6 +1,6 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.ast

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

@ -1,6 +1,6 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.ast

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

@ -1,6 +1,6 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.ast.rendering

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

@ -1,6 +1,6 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.ast.rendering

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

@ -1,6 +1,6 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.ast.rendering

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

@ -1,6 +1,6 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.ast.rendering

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

@ -1,6 +1,6 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.ast.rendering

4
kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/utils.kt
View File

@ -1,13 +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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.ast
import space.kscience.kmath.expressions.Expression
import space.kscience.kmath.expressions.MST
import space.kscience.kmath.misc.Symbol
import space.kscience.kmath.expressions.Symbol
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.IntRing

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

@ -1,6 +1,6 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.ast.rendering

11
kmath-ast/src/jsMain/kotlin/space/kscience/kmath/estree/estree.kt
View File

@ -1,6 +1,6 @@
/*
* 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.
* Use of this source code is governed by the Apache 2.0 license that can be found in the LICENSE file.
*/
package space.kscience.kmath.estree
@ -9,22 +9,23 @@ import space.kscience.kmath.estree.internal.ESTreeBuilder
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.misc.Symbol
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 = algebra.bindSymbolOrNull(node.value)
is Symbol -> {
val symbol = algebra.bindSymbolOrNull(node)
if (symbol != null)
constant(symbol)
else
variable(node.value)
variable(node.identity)
}
is Numeric -> constant(node.value)

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