kscience/kmath
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Alexander Nozik 2021-08-06 12:23:24 +03:00
commit a020d1545c
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18
.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-
@ -36,4 +38,4 @@ jobs:
restore-keys: |
${{ runner.os }}-gradle-
- name: Build
run: ./gradlew build --no-daemon --stacktrace
run: ./gradlew build --build-cache --no-daemon --stacktrace

26
.github/workflows/pages.yml vendored
View File

@ -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

32
.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-
@ -42,20 +40,14 @@ jobs:
${{ runner.os }}-gradle-
- name: Publish Windows Artifacts
if: matrix.os == 'windows-latest'
shell: cmd
run: >
./gradlew release --no-daemon
-Ppublishing.enabled=true
-Ppublishing.github.user=${{ 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 }}

9
CHANGELOG.md
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@ -2,11 +2,11 @@
## [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
@ -15,6 +15,8 @@
- `@PerformancePitfall` annotation to mark possibly slow API
- BigInt operation performance improvement and fixes by @zhelenskiy (#328)
- Unified architecture for Integration and Optimization using features.
- `BigInt` operation performance improvement and fixes by @zhelenskiy (#328)
- Integration between `MST` and Symja `IExpr`
### Changed
- Exponential operations merged with hyperbolic functions
@ -53,6 +55,7 @@
### Fixed
- Ring inherits RingOperations, not GroupOperations
- Univariate histogram filling
### Security

106
README.md
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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 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.
* **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)
>
>
@ -207,24 +224,24 @@ One can still use generic algebras though.
<hr/>
* ### [kmath-kotlingrad](kmath-kotlingrad)
> Functions, integration and interpolation
>
>
> **Maturity**: EXPERIMENTAL
>
> **Features:**
> - [differentiable-mst-expression](kmath-kotlingrad/src/main/kotlin/space/kscience/kmath/kotlingrad/DifferentiableMstExpression.kt) : MST based DifferentiableExpression.
> - [differentiable-mst-expression](kmath-kotlingrad/src/main/kotlin/space/kscience/kmath/kotlingrad/DifferentiableMstExpression.kt) : Conversions between Kotlin∇'s SFun and MST
> - [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
>
@ -241,6 +258,12 @@ One can still use generic algebras though.
> **Maturity**: EXPERIMENTAL
<hr/>
* ### [kmath-symja](kmath-symja)
>
>
> **Maturity**: PROTOTYPE
<hr/>
* ### [kmath-tensors](kmath-tensors)
>
>
@ -248,7 +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/>
@ -262,30 +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 {
@ -293,8 +319,8 @@ repositories {
}
dependencies {
api("space.kscience:kmath-core:0.3.0-dev-11")
// api("space.kscience:kmath-core-jvm:0.3.0-dev-11") 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
}
```
@ -302,7 +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.

18
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 +35,8 @@ 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("org.jetbrains.kotlinx:kotlinx-benchmark-runtime:0.3.1")
}
}
@ -41,8 +47,7 @@ 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")
//
@ -95,6 +100,11 @@ benchmark {
commonConfiguration()
include("BigIntBenchmark")
}
configurations.register("jafamaDouble") {
commonConfiguration()
include("JafamaBenchmark")
}
}
// Fix kotlinx-benchmarks bug
@ -124,3 +134,5 @@ tasks.withType<org.jetbrains.kotlin.gradle.tasks.KotlinCompile> {
readme {
maturity = ru.mipt.npm.gradle.Maturity.EXPERIMENTAL
}
addBenchmarkProperties()

46
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/ExpressionsInterpretersBenchmark.kt
View File

@ -14,22 +14,51 @@ import space.kscience.kmath.expressions.*
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.bindSymbol
import space.kscience.kmath.operations.invoke
import kotlin.math.sin
import kotlin.random.Random
@State(Scope.Benchmark)
internal class ExpressionsInterpretersBenchmark {
/**
* Benchmark case for [Expression] created with [expressionInExtendedField].
*/
@Benchmark
fun functionalExpression(blackhole: Blackhole) = invokeAndSum(functional, blackhole)
/**
* Benchmark case for [Expression] created with [toExpression].
*/
@Benchmark
fun mstExpression(blackhole: Blackhole) = invokeAndSum(mst, blackhole)
/**
* Benchmark case for [Expression] created with [compileToExpression].
*/
@Benchmark
fun asmExpression(blackhole: Blackhole) = invokeAndSum(asm, blackhole)
/**
* Benchmark case for [Expression] implemented manually with `kotlin.math` functions.
*/
@Benchmark
fun rawExpression(blackhole: Blackhole) = invokeAndSum(raw, blackhole)
/**
* Benchmark case for direct computation w/o [Expression].
*/
@Benchmark
fun justCalculate(blackhole: Blackhole) {
val random = Random(0)
var sum = 0.0
repeat(times) {
val x = random.nextDouble()
sum += x * 2.0 + 2.0 / x - 16.0 / sin(x)
}
blackhole.consume(sum)
}
private fun invokeAndSum(expr: Expression<Double>, blackhole: Blackhole) {
val random = Random(0)
var sum = 0.0
@ -42,23 +71,24 @@ 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 {
private val functional = DoubleField.expressionInExtendedField {
bindSymbol(x) * number(2.0) + number(2.0) / bindSymbol(x) - number(16.0) / sin(bindSymbol(x))
}
private val node = MstExtendedField {
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/LICENSE.txt file.
*/
package space.kscience.kmath.benchmarks
import kotlinx.benchmark.Blackhole
import org.openjdk.jmh.annotations.Benchmark
import org.openjdk.jmh.annotations.Scope
import org.openjdk.jmh.annotations.State
import space.kscience.kmath.jafama.JafamaDoubleField
import space.kscience.kmath.jafama.StrictJafamaDoubleField
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.invoke
import kotlin.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())) }
}

8
benchmarks/src/jvmMain/kotlin/space/kscience/kmath/benchmarks/MatrixInverseBenchmark.kt
View File

@ -14,8 +14,8 @@ import space.kscience.kmath.commons.linear.inverse
import space.kscience.kmath.ejml.EjmlLinearSpaceDDRM
import space.kscience.kmath.linear.InverseMatrixFeature
import space.kscience.kmath.linear.LinearSpace
import space.kscience.kmath.linear.inverseWithLup
import space.kscience.kmath.linear.invoke
import space.kscience.kmath.linear.lupSolver
import space.kscience.kmath.nd.getFeature
import kotlin.random.Random
@ -35,19 +35,19 @@ internal class MatrixInverseBenchmark {
@Benchmark
fun kmathLupInversion(blackhole: Blackhole) {
blackhole.consume(LinearSpace.double.inverseWithLup(matrix))
blackhole.consume(LinearSpace.double.lupSolver().inverse(matrix))
}
@Benchmark
fun cmLUPInversion(blackhole: Blackhole) {
with(CMLinearSpace) {
CMLinearSpace {
blackhole.consume(inverse(matrix))
}
}
@Benchmark
fun ejmlInverse(blackhole: Blackhole) {
with(EjmlLinearSpaceDDRM) {
EjmlLinearSpaceDDRM {
blackhole.consume(matrix.getFeature<InverseMatrixFeature<Double>>()?.inverse)
}
}

50
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://oss.sonatype.org/content/repositories/snapshots")
mavenCentral()
}
group = "space.kscience"
version = "0.3.0-dev-13"
version = "0.3.0-dev-14"
}
subprojects {
@ -23,30 +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.setFrom(includes + 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://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")

17
buildSrc/build.gradle.kts
View File

@ -1,5 +1,20 @@
plugins {
`kotlin-dsl`
kotlin("plugin.serialization") version "1.4.31"
}
repositories.mavenCentral()
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
View File

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

100
buildSrc/src/main/kotlin/space/kscience/kmath/benchmarks/addBenchmarkProperties.kt Normal file
View File

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

44
buildSrc/src/main/kotlin/space/kscience/kmath/ejml/codegen/ejmlCodegen.kt
View File

@ -14,12 +14,12 @@ private fun Appendable.appendEjmlVector(type: String, ejmlMatrixType: String) {
@Language("kotlin") val text = """/**
* [EjmlVector] specialization for [$type].
*/
public class Ejml${type}Vector<out M : $ejmlMatrixType>(public override val origin: M) : EjmlVector<$type, M>(origin) {
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" }
}
public override operator fun get(index: Int): $type = origin[0, index]
override operator fun get(index: Int): $type = origin[0, index]
}"""
appendLine(text)
appendLine()
@ -29,8 +29,8 @@ private fun Appendable.appendEjmlMatrix(type: String, ejmlMatrixType: String) {
val text = """/**
* [EjmlMatrix] specialization for [$type].
*/
public class Ejml${type}Matrix<out M : $ejmlMatrixType>(public override val origin: M) : EjmlMatrix<$type, M>(origin) {
public override operator fun get(i: Int, j: Int): $type = origin[i, j]
public class Ejml${type}Matrix<out M : $ejmlMatrixType>(override val origin: M) : EjmlMatrix<$type, M>(origin) {
override operator fun get(i: Int, j: Int): $type = origin[i, j]
}"""
appendLine(text)
appendLine()
@ -54,23 +54,23 @@ public object EjmlLinearSpace${ops} : EjmlLinearSpace<${type}, ${kmathAlgebra},
/**
* The [${kmathAlgebra}] reference.
*/
public override val elementAlgebra: $kmathAlgebra get() = $kmathAlgebra
override val elementAlgebra: $kmathAlgebra get() = $kmathAlgebra
@Suppress("UNCHECKED_CAST")
public override fun Matrix<${type}>.toEjml(): Ejml${type}Matrix<${ejmlMatrixType}> = when {
override fun Matrix<${type}>.toEjml(): Ejml${type}Matrix<${ejmlMatrixType}> = when {
this is Ejml${type}Matrix<*> && origin is $ejmlMatrixType -> this as Ejml${type}Matrix<${ejmlMatrixType}>
else -> buildMatrix(rowNum, colNum) { i, j -> get(i, j) }
}
@Suppress("UNCHECKED_CAST")
public override fun Point<${type}>.toEjml(): Ejml${type}Vector<${ejmlMatrixType}> = when {
override fun Point<${type}>.toEjml(): Ejml${type}Vector<${ejmlMatrixType}> = when {
this is Ejml${type}Vector<*> && origin is $ejmlMatrixType -> this as Ejml${type}Vector<${ejmlMatrixType}>
else -> Ejml${type}Vector(${ejmlMatrixType}(size, 1).also {
(0 until it.numRows).forEach { row -> it[row, 0] = get(row) }
})
}
public override fun buildMatrix(
override fun buildMatrix(
rows: Int,
columns: Int,
initializer: ${kmathAlgebra}.(i: Int, j: Int) -> ${type},
@ -80,7 +80,7 @@ public object EjmlLinearSpace${ops} : EjmlLinearSpace<${type}, ${kmathAlgebra},
}
}.wrapMatrix()
public override fun buildVector(
override fun buildVector(
size: Int,
initializer: ${kmathAlgebra}.(Int) -> ${type},
): Ejml${type}Vector<${ejmlMatrixType}> = Ejml${type}Vector(${ejmlMatrixType}(size, 1).also {
@ -90,21 +90,21 @@ public object EjmlLinearSpace${ops} : EjmlLinearSpace<${type}, ${kmathAlgebra},
private fun <T : ${ejmlMatrixParentTypeMatrix}> T.wrapMatrix() = Ejml${type}Matrix(this)
private fun <T : ${ejmlMatrixParentTypeMatrix}> T.wrapVector() = Ejml${type}Vector(this)
public override fun Matrix<${type}>.unaryMinus(): Matrix<${type}> = this * elementAlgebra { -one }
override fun Matrix<${type}>.unaryMinus(): Matrix<${type}> = this * elementAlgebra { -one }
public override fun Matrix<${type}>.dot(other: Matrix<${type}>): Ejml${type}Matrix<${ejmlMatrixType}> {
override fun Matrix<${type}>.dot(other: Matrix<${type}>): Ejml${type}Matrix<${ejmlMatrixType}> {
val out = ${ejmlMatrixType}(1, 1)
CommonOps_${ops}.mult(toEjml().origin, other.toEjml().origin, out)
return out.wrapMatrix()
}
public override fun Matrix<${type}>.dot(vector: Point<${type}>): Ejml${type}Vector<${ejmlMatrixType}> {
override fun Matrix<${type}>.dot(vector: Point<${type}>): Ejml${type}Vector<${ejmlMatrixType}> {
val out = ${ejmlMatrixType}(1, 1)
CommonOps_${ops}.mult(toEjml().origin, vector.toEjml().origin, out)
return out.wrapVector()
}
public override operator fun Matrix<${type}>.minus(other: Matrix<${type}>): Ejml${type}Matrix<${ejmlMatrixType}> {
override operator fun Matrix<${type}>.minus(other: Matrix<${type}>): Ejml${type}Matrix<${ejmlMatrixType}> {
val out = ${ejmlMatrixType}(1, 1)
CommonOps_${ops}.add(
@ -123,19 +123,19 @@ public object EjmlLinearSpace${ops} : EjmlLinearSpace<${type}, ${kmathAlgebra},
return out.wrapMatrix()
}
public override operator fun Matrix<${type}>.times(value: ${type}): Ejml${type}Matrix<${ejmlMatrixType}> {
override operator fun Matrix<${type}>.times(value: ${type}): Ejml${type}Matrix<${ejmlMatrixType}> {
val res = ${ejmlMatrixType}(1, 1)
CommonOps_${ops}.scale(value, toEjml().origin, res)
return res.wrapMatrix()
}
public override fun Point<${type}>.unaryMinus(): Ejml${type}Vector<${ejmlMatrixType}> {
override fun Point<${type}>.unaryMinus(): Ejml${type}Vector<${ejmlMatrixType}> {
val res = ${ejmlMatrixType}(1, 1)
CommonOps_${ops}.changeSign(toEjml().origin, res)
return res.wrapVector()
}
public override fun Matrix<${type}>.plus(other: Matrix<${type}>): Ejml${type}Matrix<${ejmlMatrixType}> {
override fun Matrix<${type}>.plus(other: Matrix<${type}>): Ejml${type}Matrix<${ejmlMatrixType}> {
val out = ${ejmlMatrixType}(1, 1)
CommonOps_${ops}.add(
@ -154,7 +154,7 @@ public object EjmlLinearSpace${ops} : EjmlLinearSpace<${type}, ${kmathAlgebra},
return out.wrapMatrix()
}
public override fun Point<${type}>.plus(other: Point<${type}>): Ejml${type}Vector<${ejmlMatrixType}> {
override fun Point<${type}>.plus(other: Point<${type}>): Ejml${type}Vector<${ejmlMatrixType}> {
val out = ${ejmlMatrixType}(1, 1)
CommonOps_${ops}.add(
@ -173,7 +173,7 @@ public object EjmlLinearSpace${ops} : EjmlLinearSpace<${type}, ${kmathAlgebra},
return out.wrapVector()
}
public override fun Point<${type}>.minus(other: Point<${type}>): Ejml${type}Vector<${ejmlMatrixType}> {
override fun Point<${type}>.minus(other: Point<${type}>): Ejml${type}Vector<${ejmlMatrixType}> {
val out = ${ejmlMatrixType}(1, 1)
CommonOps_${ops}.add(
@ -192,18 +192,18 @@ public object EjmlLinearSpace${ops} : EjmlLinearSpace<${type}, ${kmathAlgebra},
return out.wrapVector()
}
public override fun ${type}.times(m: Matrix<${type}>): Ejml${type}Matrix<${ejmlMatrixType}> = m * this
override fun ${type}.times(m: Matrix<${type}>): Ejml${type}Matrix<${ejmlMatrixType}> = m * this
public override fun Point<${type}>.times(value: ${type}): Ejml${type}Vector<${ejmlMatrixType}> {
override fun Point<${type}>.times(value: ${type}): Ejml${type}Vector<${ejmlMatrixType}> {
val res = ${ejmlMatrixType}(1, 1)
CommonOps_${ops}.scale(value, toEjml().origin, res)
return res.wrapVector()
}
public override fun ${type}.times(v: Point<${type}>): Ejml${type}Vector<${ejmlMatrixType}> = v * this
override fun ${type}.times(v: Point<${type}>): Ejml${type}Vector<${ejmlMatrixType}> = v * this
@UnstableKMathAPI
public override fun <F : StructureFeature> computeFeature(structure: Matrix<${type}>, type: KClass<out F>): F? {
override fun <F : StructureFeature> computeFeature(structure: Matrix<${type}>, type: KClass<out F>): F? {
structure.getFeature(type)?.let { return it }
val origin = structure.toEjml().origin

83
docs/algebra.md
View File

@ -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
docs/buffers.md
View File

@ -1,17 +1,20 @@
# 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 .

19
docs/codestyle.md
View File

@ -1,26 +1,20 @@
# 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.

44
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@ -2,18 +2,17 @@
## 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

23
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@ -1,26 +1,21 @@
# 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**

14
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@ -1,14 +0,0 @@
# 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

32
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@ -1,19 +1,31 @@
## 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

20
docs/nd-structure.md
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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).

14
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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

68
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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 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.
* **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.

8
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@ -26,7 +26,7 @@ dependencies {
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 +41,11 @@ 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")
implementation("space.kscience:plotlykt-server:0.4.2")
//jafama
implementation(project(":kmath-jafama"))
}
kotlin.sourceSets.all {

2
examples/src/main/kotlin/space/kscience/kmath/ast/astRendering.kt
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@ -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:")

2
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@ -9,12 +9,10 @@ import space.kscience.kmath.expressions.MstField
import space.kscience.kmath.expressions.Symbol.Companion.x
import space.kscience.kmath.expressions.interpret
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.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
}

18
examples/src/main/kotlin/space/kscience/kmath/ast/kotlingradSupport.kt
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@ -5,25 +5,23 @@
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.expressions.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()
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
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@ -0,0 +1,27 @@
/*
* Copyright 2018-2021 KMath contributors.
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
package space.kscience.kmath.ast
import space.kscience.kmath.expressions.Symbol.Companion.x
import space.kscience.kmath.expressions.derivative
import space.kscience.kmath.expressions.invoke
import space.kscience.kmath.expressions.toExpression
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.symja.toSymjaExpression
/**
* In this example, *x<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))
}

27
examples/src/main/kotlin/space/kscience/kmath/commons/fit/fitWithAutoDiff.kt
View File

@ -7,11 +7,15 @@ package space.kscience.kmath.commons.fit
import kotlinx.html.br
import kotlinx.html.h3
import space.kscience.kmath.commons.optimization.chiSquared
import space.kscience.kmath.commons.expressions.DSProcessor
import space.kscience.kmath.commons.optimization.CMOptimizer
import space.kscience.kmath.distributions.NormalDistribution
import space.kscience.kmath.expressions.chiSquaredExpression
import space.kscience.kmath.expressions.symbol
import space.kscience.kmath.optimization.FunctionOptimization
import space.kscience.kmath.optimization.OptimizationResult
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
@ -24,8 +28,7 @@ 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
@ -64,17 +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 {
@ -91,7 +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"
}
}
@ -100,7 +107,7 @@ suspend fun main() {
+"Fit result: $result"
}
h3 {
+"Chi2/dof = ${result.value / (x.size - 3)}"
+"Chi2/dof = ${result.resultValue / (x.size - 3)}"
}
}

15
examples/src/main/kotlin/space/kscience/kmath/jafama/JafamaDemo.kt Normal file
View File

@ -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/LICENSE.txt 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) })
}

3
examples/src/main/kotlin/space/kscience/kmath/stat/DistributionDemo.kt
View File

@ -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)
/**

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

@ -32,7 +32,7 @@ fun main() {
// 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
// specialized nd-field for Double. It works as generic Double field as well.
val realField = AlgebraND.real(dim, dim)
//A generic boxing field. It should be used for objects, not primitives.
val boxingField = AlgebraND.field(DoubleField, Buffer.Companion::boxing, dim, dim)

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

@ -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() {

4
examples/src/main/kotlin/space/kscience/kmath/tensors/DataSetNormalization.kt
View File

@ -17,7 +17,7 @@ fun main() = BroadcastDoubleTensorAlgebra { // work in context with broadcast m
dataset += fromArray(
intArrayOf(5),
doubleArrayOf(0.0, 1.0, 1.5, 3.0, 5.0) // rows means
doubleArrayOf(0.0, 1.0, 1.5, 3.0, 5.0) // row means
)
@ -28,7 +28,7 @@ fun main() = BroadcastDoubleTensorAlgebra { // work in context with broadcast m
println("Mean:\n$mean")
println("Standard deviation:\n$std")
// also we can calculate other statistic as minimum and maximum of rows
// 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)}")

2
examples/src/main/kotlin/space/kscience/kmath/tensors/LinearSystemSolvingWithLUP.kt
View File

@ -42,7 +42,7 @@ fun main() = BroadcastDoubleTensorAlgebra {// work in context with linear operat
// get P, L, U such that PA = LU
val (p, l, u) = a.lu()
// check that P is permutation matrix
// check P is permutation matrix
println("P:\n$p")
// L is lower triangular matrix and U is upper triangular matrix
println("L:\n$l")

2
examples/src/main/kotlin/space/kscience/kmath/tensors/NeuralNetwork.kt
View File

@ -186,7 +186,7 @@ fun main() = BroadcastDoubleTensorAlgebra {
x += fromArray(
intArrayOf(5),
doubleArrayOf(0.0, -1.0, -2.5, -3.0, 5.5) // rows means
doubleArrayOf(0.0, -1.0, -2.5, -3.0, 5.5) // row means
)

4
examples/src/main/kotlin/space/kscience/kmath/tensors/PCA.kt
View File

@ -65,8 +65,8 @@ fun main(): Unit = BroadcastDoubleTensorAlgebra { // work in context with broad
val datasetReduced = v dot stack(listOf(xScaled, yScaled))
println("Reduced data:\n$datasetReduced")
// we can restore original data from reduced data.
// for example, find 7th element of dataset
// we can restore original data from reduced data;
// for example, find 7th element of dataset.
val n = 7
val restored = (datasetReduced[n] dot v.view(intArrayOf(1, 2))) * std + mean
println("Original value:\n${dataset[n]}")

3
gradle.properties
View File

@ -7,7 +7,6 @@ kotlin.code.style=official
kotlin.mpp.enableGranularSourceSetsMetadata=true
kotlin.mpp.stability.nowarn=true
kotlin.native.enableDependencyPropagation=false
kotlin.parallel.tasks.in.project=true
org.gradle.configureondemand=true
org.gradle.jvmargs=-XX:MaxMetaspaceSize=1G
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-11`.
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-11'
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-11")
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):

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

@ -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) {

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

@ -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>")

10
kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/MathRenderer.kt
View File

@ -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,

72
kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/MathSyntax.kt
View File

@ -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 {

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

@ -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
*/

53
kmath-ast/src/commonMain/kotlin/space/kscience/kmath/ast/rendering/features.kt
View File

@ -13,15 +13,14 @@ 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 Symbol) null
else SymbolSyntax(string = node.identity)
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
@ -141,7 +140,7 @@ 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? =
override fun render(renderer: FeaturedMathRenderer, node: MST): MathSyntax? =
if (node !is Symbol || node.identity !in symbols)
null
else
@ -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")),

91
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

@ -5,6 +5,7 @@
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

5
kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/TestCompilerConsistencyWithInterpreter.kt
View File

@ -11,7 +11,6 @@ import space.kscience.kmath.expressions.Symbol.Companion.x
import space.kscience.kmath.expressions.interpret
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.IntRing
import space.kscience.kmath.operations.bindSymbol
import space.kscience.kmath.operations.invoke
import kotlin.test.Test
import kotlin.test.assertEquals
@ -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

7
kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/TestCompilerOperations.kt
View File

@ -9,7 +9,6 @@ import space.kscience.kmath.expressions.MstExtendedField
import space.kscience.kmath.expressions.Symbol.Companion.x
import space.kscience.kmath.expressions.invoke
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.bindSymbol
import space.kscience.kmath.operations.invoke
import kotlin.test.Test
import kotlin.test.assertEquals
@ -47,19 +46,19 @@ internal class TestCompilerOperations {
@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))
}
}

5
kmath-ast/src/commonTest/kotlin/space/kscience/kmath/ast/TestCompilerVariables.kt
View File

@ -9,7 +9,6 @@ import space.kscience.kmath.expressions.MstRing
import space.kscience.kmath.expressions.Symbol.Companion.x
import space.kscience.kmath.expressions.invoke
import space.kscience.kmath.operations.IntRing
import space.kscience.kmath.operations.bindSymbol
import space.kscience.kmath.operations.invoke
import kotlin.test.Test
import kotlin.test.assertEquals
@ -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/jsMain/kotlin/space/kscience/kmath/internal/estree/estree.kt
View File

@ -3,6 +3,8 @@
* Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
*/
@file:Suppress("ClassName")
package space.kscience.kmath.internal.estree
import kotlin.js.RegExp

6
kmath-ast/src/jsMain/kotlin/space/kscience/kmath/wasm/internal/WasmBuilder.kt
View File

@ -27,11 +27,7 @@ internal sealed class WasmBuilder<T>(
lateinit var ctx: BinaryenModule
open fun visitSymbolic(mst: Symbol): ExpressionRef {
try {
algebra.bindSymbol(mst)
} catch (ignored: Throwable) {
null
}?.let { return visitNumeric(Numeric(it)) }
algebra.bindSymbolOrNull(mst)?.let { return visitNumeric(Numeric(it)) }
var idx = keys.indexOf(mst)

51
kmath-ast/src/jsTest/kotlin/space/kscience/kmath/ast/TestExecutionTime.kt
View File

@ -7,7 +7,6 @@ package space.kscience.kmath.ast
import space.kscience.kmath.expressions.*
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.ExtendedField
import space.kscience.kmath.operations.bindSymbol
import space.kscience.kmath.operations.invoke
import kotlin.math.sin
@ -17,18 +16,19 @@ import kotlin.time.measureTime
import space.kscience.kmath.estree.compileToExpression as estreeCompileToExpression
import space.kscience.kmath.wasm.compileToExpression as wasmCompileToExpression
// TODO move to benchmarks when https://github.com/Kotlin/kotlinx-benchmark/pull/38 or similar feature is merged
internal class TestExecutionTime {
private companion object {
private const val times = 1_000_000
private val x by symbol
private val algebra: ExtendedField<Double> = DoubleField
private val algebra = DoubleField
private val functional = DoubleField.expressionInExtendedField {
bindSymbol(x) * const(2.0) + const(2.0) / bindSymbol(x) - const(16.0) / sin(bindSymbol(x))
}
private val node = MstExtendedField {
bindSymbol(x) * number(2.0) + number(2.0) / bindSymbol(x) - number(16.0) / sin(bindSymbol(x))
x * number(2.0) + number(2.0) / x - number(16.0) / sin(x)
}
private val mst = node.toExpression(DoubleField)
@ -43,7 +43,13 @@ internal class TestExecutionTime {
// };
private val raw = Expression<Double> { args ->
args.getValue(x) * 2.0 + 2.0 / args.getValue(x) - 16.0 / sin(args.getValue(x))
val x = args[x]!!
x * 2.0 + 2.0 / x - 16.0 / sin(x)
}
private val justCalculate = { args: dynamic ->
val x = args[x].unsafeCast<Double>()
x * 2.0 + 2.0 / x - 16.0 / sin(x)
}
}
@ -51,21 +57,56 @@ internal class TestExecutionTime {
println(name)
val rng = Random(0)
var sum = 0.0
measureTime { repeat(times) { sum += expr(x to rng.nextDouble()) } }.also(::println)
measureTime {
repeat(times) { sum += expr(x to rng.nextDouble()) }
}.also(::println)
}
/**
* [Expression] created with [expressionInExtendedField].
*/
@Test
fun functionalExpression() = invokeAndSum("functional", functional)
/**
* [Expression] created with [mstExpression].
*/
@Test
fun mstExpression() = invokeAndSum("mst", mst)
/**
* [Expression] created with [wasmCompileToExpression].
*/
@Test
fun wasmExpression() = invokeAndSum("wasm", wasm)
/**
* [Expression] created with [estreeCompileToExpression].
*/
@Test
fun estreeExpression() = invokeAndSum("estree", wasm)
/**
* [Expression] implemented manually with `kotlin.math`.
*/
@Test
fun rawExpression() = invokeAndSum("raw", raw)
/**
* Direct computation w/o [Expression].
*/
@Test
fun justCalculateExpression() {
println("justCalculate")
val rng = Random(0)
var sum = 0.0
measureTime {
repeat(times) {
val arg = rng.nextDouble()
val o = js("{}")
o["x"] = arg
sum += justCalculate(o)
}
}.also(::println)
}
}

3
kmath-ast/src/jsTest/kotlin/space/kscience/kmath/ast/utils.kt
View File

@ -10,6 +10,8 @@ import space.kscience.kmath.expressions.MST
import space.kscience.kmath.expressions.Symbol
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.IntRing
import kotlin.contracts.InvocationKind
import kotlin.contracts.contract
import space.kscience.kmath.estree.compile as estreeCompile
import space.kscience.kmath.estree.compileToExpression as estreeCompileToExpression
import space.kscience.kmath.wasm.compile as wasmCompile
@ -34,6 +36,7 @@ private object ESTreeCompilerTestContext : CompilerTestContext {
}
internal actual inline fun runCompilerTest(action: CompilerTestContext.() -> Unit) {
contract { callsInPlace(action, InvocationKind.AT_LEAST_ONCE) }
action(WasmCompilerTestContext)
action(ESTreeCompilerTestContext)
}

5
kmath-ast/src/jsTest/kotlin/space/kscience/kmath/wasm/TestWasmSpecific.kt
View File

@ -11,7 +11,6 @@ import space.kscience.kmath.expressions.invoke
import space.kscience.kmath.expressions.symbol
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
@ -31,7 +30,7 @@ internal class TestWasmSpecific {
@Test
fun argsPassing() {
val res = MstExtendedField { bindSymbol(y) + bindSymbol(x).pow(10) }.compile(
val res = MstExtendedField { y + x.pow(10) }.compile(
DoubleField,
x to 2.0,
y to 100000000.0,
@ -42,7 +41,7 @@ internal class TestWasmSpecific {
@Test
fun powFunction() {
val expr = MstExtendedField { bindSymbol(x).pow(1.0 / 6.0) }.compileToExpression(DoubleField)
val expr = MstExtendedField { x.pow(1.0 / 6.0) }.compileToExpression(DoubleField)
assertEquals(0.9730585187140817, expr(x to 0.8488554755054833))
}

4
kmath-ast/src/jvmMain/kotlin/space/kscience/kmath/asm/internal/codegenUtils.kt
View File

@ -57,13 +57,13 @@ internal fun MethodVisitor.label(): Label = Label().also(::visitLabel)
/**
* Creates a class name for [Expression] subclassed to implement [mst] provided.
*
* This methods helps to avoid collisions of class name to prevent loading several classes with the same name. If there
* These methods help to avoid collisions of class name to prevent loading several classes with the same name. If there
* is a colliding class, change [collision] parameter or leave it `0` to check existing classes recursively.
*
* @author Iaroslav Postovalov
*/
internal tailrec fun buildName(mst: MST, collision: Int = 0): String {
val name = "kscience.kmath.asm.generated.AsmCompiledExpression_${mst.hashCode()}_$collision"
val name = "space.kscience.kmath.asm.generated.CompiledExpression_${mst.hashCode()}_$collision"
try {
Class.forName(name)

7
kmath-ast/src/jvmTest/kotlin/space/kscience/kmath/ast/utils.kt
View File

@ -10,6 +10,8 @@ import space.kscience.kmath.expressions.MST
import space.kscience.kmath.expressions.Symbol
import space.kscience.kmath.operations.DoubleField
import space.kscience.kmath.operations.IntRing
import kotlin.contracts.InvocationKind
import kotlin.contracts.contract
import space.kscience.kmath.asm.compile as asmCompile
import space.kscience.kmath.asm.compileToExpression as asmCompileToExpression
@ -22,4 +24,7 @@ private object AsmCompilerTestContext : CompilerTestContext {
asmCompile(algebra, arguments)
}
internal actual inline fun runCompilerTest(action: CompilerTestContext.() -> Unit) = action(AsmCompilerTestContext)
internal actual inline fun runCompilerTest(action: CompilerTestContext.() -> Unit) {
contract { callsInPlace(action, InvocationKind.EXACTLY_ONCE) }
action(AsmCompilerTestContext)
}

81
kmath-commons/src/main/kotlin/space/kscience/kmath/commons/expressions/DerivativeStructureExpression.kt
View File

@ -15,7 +15,7 @@ import space.kscience.kmath.operations.NumbersAddOperations
* A field over commons-math [DerivativeStructure].
*
* @property order The derivation order.
* @property bindings The map of bindings values. All bindings are considered free parameters
* @param bindings The map of bindings values. All bindings are considered free parameters
*/
@OptIn(UnstableKMathAPI::class)
public class DerivativeStructureField(
@ -25,13 +25,13 @@ public class DerivativeStructureField(
NumbersAddOperations<DerivativeStructure> {
public val numberOfVariables: Int = bindings.size
public override val zero: DerivativeStructure by lazy { DerivativeStructure(numberOfVariables, order) }
public override val one: DerivativeStructure by lazy { DerivativeStructure(numberOfVariables, order, 1.0) }
override val zero: DerivativeStructure by lazy { DerivativeStructure(numberOfVariables, order) }
override val one: DerivativeStructure by lazy { DerivativeStructure(numberOfVariables, order, 1.0) }
public override fun number(value: Number): DerivativeStructure = const(value.toDouble())
override fun number(value: Number): DerivativeStructure = const(value.toDouble())
/**
* A class that implements both [DerivativeStructure] and a [Symbol]
* A class implementing both [DerivativeStructure] and [Symbol].
*/
public inner class DerivativeStructureSymbol(
size: Int,
@ -39,10 +39,10 @@ public class DerivativeStructureField(
symbol: Symbol,
value: Double,
) : DerivativeStructure(size, order, index, value), Symbol {
public override val identity: String = symbol.identity
public override fun toString(): String = identity
public override fun equals(other: Any?): Boolean = this.identity == (other as? Symbol)?.identity
public override fun hashCode(): Int = identity.hashCode()
override val identity: String = symbol.identity
override fun toString(): String = identity
override fun equals(other: Any?): Boolean = this.identity == (other as? Symbol)?.identity
override fun hashCode(): Int = identity.hashCode()
}
/**
@ -52,10 +52,10 @@ public class DerivativeStructureField(
key.identity to DerivativeStructureSymbol(numberOfVariables, index, key, value)
}.toMap()
public override fun const(value: Double): DerivativeStructure = DerivativeStructure(numberOfVariables, order, value)
override fun const(value: Double): DerivativeStructure = DerivativeStructure(numberOfVariables, order, value)
public override fun bindSymbolOrNull(value: String): DerivativeStructureSymbol? = variables[value]
public override fun bindSymbol(value: String): DerivativeStructureSymbol = variables.getValue(value)
override fun bindSymbolOrNull(value: String): DerivativeStructureSymbol? = variables[value]
override fun bindSymbol(value: String): DerivativeStructureSymbol = variables.getValue(value)
public fun bindSymbolOrNull(symbol: Symbol): DerivativeStructureSymbol? = variables[symbol.identity]
public fun bindSymbol(symbol: Symbol): DerivativeStructureSymbol = variables.getValue(symbol.identity)
@ -68,45 +68,48 @@ public class DerivativeStructureField(
public fun DerivativeStructure.derivative(vararg symbols: Symbol): Double = derivative(symbols.toList())
public override fun DerivativeStructure.unaryMinus(): DerivativeStructure = negate()
override fun DerivativeStructure.unaryMinus(): DerivativeStructure = negate()
public override fun add(a: DerivativeStructure, b: DerivativeStructure): DerivativeStructure = a.add(b)
override fun add(a: DerivativeStructure, b: DerivativeStructure): DerivativeStructure = a.add(b)
public override fun scale(a: DerivativeStructure, value: Double): DerivativeStructure = a.multiply(value)
override fun scale(a: DerivativeStructure, value: Double): DerivativeStructure = a.multiply(value)
public override fun multiply(a: DerivativeStructure, b: DerivativeStructure): DerivativeStructure = a.multiply(b)
public override fun divide(a: DerivativeStructure, b: DerivativeStructure): DerivativeStructure = a.divide(b)
public override fun sin(arg: DerivativeStructure): DerivativeStructure = arg.sin()
public override fun cos(arg: DerivativeStructure): DerivativeStructure = arg.cos()
public override fun tan(arg: DerivativeStructure): DerivativeStructure = arg.tan()
public override fun asin(arg: DerivativeStructure): DerivativeStructure = arg.asin()
public override fun acos(arg: DerivativeStructure): DerivativeStructure = arg.acos()
public override fun atan(arg: DerivativeStructure): DerivativeStructure = arg.atan()
public override fun sinh(arg: DerivativeStructure): DerivativeStructure = arg.sinh()
public override fun cosh(arg: DerivativeStructure): DerivativeStructure = arg.cosh()
public override fun tanh(arg: DerivativeStructure): DerivativeStructure = arg.tanh()
public override fun asinh(arg: DerivativeStructure): DerivativeStructure = arg.asinh()
public override fun acosh(arg: DerivativeStructure): DerivativeStructure = arg.acosh()
public override fun atanh(arg: DerivativeStructure): DerivativeStructure = arg.atanh()
override fun multiply(a: DerivativeStructure, b: DerivativeStructure): DerivativeStructure = a.multiply(b)
override fun divide(a: DerivativeStructure, b: DerivativeStructure): DerivativeStructure = a.divide(b)
override fun sin(arg: DerivativeStructure): DerivativeStructure = arg.sin()
override fun cos(arg: DerivativeStructure): DerivativeStructure = arg.cos()
override fun tan(arg: DerivativeStructure): DerivativeStructure = arg.tan()
override fun asin(arg: DerivativeStructure): DerivativeStructure = arg.asin()
override fun acos(arg: DerivativeStructure): DerivativeStructure = arg.acos()
override fun atan(arg: DerivativeStructure): DerivativeStructure = arg.atan()
override fun sinh(arg: DerivativeStructure): DerivativeStructure = arg.sinh()
override fun cosh(arg: DerivativeStructure): DerivativeStructure = arg.cosh()
override fun tanh(arg: DerivativeStructure): DerivativeStructure = arg.tanh()
override fun asinh(arg: DerivativeStructure): DerivativeStructure = arg.asinh()
override fun acosh(arg: DerivativeStructure): DerivativeStructure = arg.acosh()
override fun atanh(arg: DerivativeStructure): DerivativeStructure = arg.atanh()
public override fun power(arg: DerivativeStructure, pow: Number): DerivativeStructure = when (pow) {
override fun power(arg: DerivativeStructure, pow: Number): DerivativeStructure = when (pow) {
is Double -> arg.pow(pow)
is Int -> arg.pow(pow)
else -> arg.pow(pow.toDouble())
}
public fun power(arg: DerivativeStructure, pow: DerivativeStructure): DerivativeStructure = arg.pow(pow)
public override fun exp(arg: DerivativeStructure): DerivativeStructure = arg.exp()
public override fun ln(arg: DerivativeStructure): DerivativeStructure = arg.log()
override fun exp(arg: DerivativeStructure): DerivativeStructure = arg.exp()
override fun ln(arg: DerivativeStructure): DerivativeStructure = arg.log()
public override operator fun DerivativeStructure.plus(b: Number): DerivativeStructure = add(b.toDouble())
public override operator fun DerivativeStructure.minus(b: Number): DerivativeStructure = subtract(b.toDouble())
public override operator fun Number.plus(b: DerivativeStructure): DerivativeStructure = b + this
public override operator fun Number.minus(b: DerivativeStructure): DerivativeStructure = b - this
override operator fun DerivativeStructure.plus(b: Number): DerivativeStructure = add(b.toDouble())
override operator fun DerivativeStructure.minus(b: Number): DerivativeStructure = subtract(b.toDouble())
override operator fun Number.plus(b: DerivativeStructure): DerivativeStructure = b + this
override operator fun Number.minus(b: DerivativeStructure): DerivativeStructure = b - this
}
/**
* Auto-diff processor based on Commons-math [DerivativeStructure]
*/
public object DSProcessor : AutoDiffProcessor<Double, DerivativeStructure, DerivativeStructureField> {
public override fun differentiate(
override fun differentiate(
function: DerivativeStructureField.() -> DerivativeStructure,
): DerivativeStructureExpression = DerivativeStructureExpression(function)
}
@ -117,13 +120,13 @@ public object DSProcessor : AutoDiffProcessor<Double, DerivativeStructure, Deriv
public class DerivativeStructureExpression(
public val function: DerivativeStructureField.() -> DerivativeStructure,
) : DifferentiableExpression<Double> {
public override operator fun invoke(arguments: Map<Symbol, Double>): Double =
override operator fun invoke(arguments: Map<Symbol, Double>): Double =
DerivativeStructureField(0, arguments).function().value
/**
* Get the derivative expression with given orders
*/
public override fun derivativeOrNull(symbols: List<Symbol>): Expression<Double> = Expression { arguments ->
override fun derivativeOrNull(symbols: List<Symbol>): Expression<Double> = Expression { arguments ->
with(DerivativeStructureField(symbols.size, arguments)) { function().derivative(symbols) }
}
}

22
kmath-commons/src/main/kotlin/space/kscience/kmath/commons/linear/CMMatrix.kt
View File

@ -15,18 +15,18 @@ import kotlin.reflect.KClass
import kotlin.reflect.cast
public class CMMatrix(public val origin: RealMatrix) : Matrix<Double> {
public override val rowNum: Int get() = origin.rowDimension
public override val colNum: Int get() = origin.columnDimension
override val rowNum: Int get() = origin.rowDimension
override val colNum: Int get() = origin.columnDimension
public override operator fun get(i: Int, j: Int): Double = origin.getEntry(i, j)
override operator fun get(i: Int, j: Int): Double = origin.getEntry(i, j)
}
public class CMVector(public val origin: RealVector) : Point<Double> {
public override val size: Int get() = origin.dimension
override val size: Int get() = origin.dimension
public override operator fun get(index: Int): Double = origin.getEntry(index)
override operator fun get(index: Int): Double = origin.getEntry(index)
public override operator fun iterator(): Iterator<Double> = origin.toArray().iterator()
override operator fun iterator(): Iterator<Double> = origin.toArray().iterator()
}
public fun RealVector.toPoint(): CMVector = CMVector(this)
@ -34,7 +34,7 @@ public fun RealVector.toPoint(): CMVector = CMVector(this)
public object CMLinearSpace : LinearSpace<Double, DoubleField> {
override val elementAlgebra: DoubleField get() = DoubleField
public override fun buildMatrix(
override fun buildMatrix(
rows: Int,
columns: Int,
initializer: DoubleField.(i: Int, j: Int) -> Double,
@ -73,16 +73,16 @@ public object CMLinearSpace : LinearSpace<Double, DoubleField> {
override fun Point<Double>.minus(other: Point<Double>): CMVector =
toCM().origin.subtract(other.toCM().origin).wrap()
public override fun Matrix<Double>.dot(other: Matrix<Double>): CMMatrix =
override fun Matrix<Double>.dot(other: Matrix<Double>): CMMatrix =
toCM().origin.multiply(other.toCM().origin).wrap()
public override fun Matrix<Double>.dot(vector: Point<Double>): CMVector =
override fun Matrix<Double>.dot(vector: Point<Double>): CMVector =
toCM().origin.preMultiply(vector.toCM().origin).wrap()
public override operator fun Matrix<Double>.minus(other: Matrix<Double>): CMMatrix =
override operator fun Matrix<Double>.minus(other: Matrix<Double>): CMMatrix =
toCM().origin.subtract(other.toCM().origin).wrap()
public override operator fun Matrix<Double>.times(value: Double): CMMatrix =
override operator fun Matrix<Double>.times(value: Double): CMMatrix =
toCM().origin.scalarMultiply(value).wrap()
override fun Double.times(m: Matrix<Double>): CMMatrix =

32
kmath-commons/src/main/kotlin/space/kscience/kmath/commons/random/CMRandomGeneratorWrapper.kt
View File

@ -5,35 +5,43 @@
package space.kscience.kmath.commons.random
import kotlinx.coroutines.runBlocking
import space.kscience.kmath.misc.PerformancePitfall
import space.kscience.kmath.samplers.GaussianSampler
import space.kscience.kmath.misc.toIntExact
import space.kscience.kmath.stat.RandomGenerator
import space.kscience.kmath.stat.next
public class CMRandomGeneratorWrapper(
public val factory: (IntArray) -> RandomGenerator,
) : org.apache.commons.math3.random.RandomGenerator {
private var generator: RandomGenerator = factory(intArrayOf())
public override fun nextBoolean(): Boolean = generator.nextBoolean()
public override fun nextFloat(): Float = generator.nextDouble().toFloat()
override fun nextBoolean(): Boolean = generator.nextBoolean()
override fun nextFloat(): Float = generator.nextDouble().toFloat()
public override fun setSeed(seed: Int) {
override fun setSeed(seed: Int) {
generator = factory(intArrayOf(seed))
}
public override fun setSeed(seed: IntArray) {
override fun setSeed(seed: IntArray) {
generator = factory(seed)
}
public override fun setSeed(seed: Long) {
setSeed(seed.toInt())
override fun setSeed(seed: Long) {
setSeed(seed.toIntExact())
}
public override fun nextBytes(bytes: ByteArray) {
override fun nextBytes(bytes: ByteArray) {
generator.fillBytes(bytes)
}
public override fun nextInt(): Int = generator.nextInt()
public override fun nextInt(n: Int): Int = generator.nextInt(n)
public override fun nextGaussian(): Double = TODO()
public override fun nextDouble(): Double = generator.nextDouble()
public override fun nextLong(): Long = generator.nextLong()
override fun nextInt(): Int = generator.nextInt()
override fun nextInt(n: Int): Int = generator.nextInt(n)
@PerformancePitfall
override fun nextGaussian(): Double = runBlocking { GaussianSampler(0.0, 1.0).next(generator) }
override fun nextDouble(): Double = generator.nextDouble()
override fun nextLong(): Long = generator.nextLong()
}

2
kmath-commons/src/main/kotlin/space/kscience/kmath/commons/transform/Transformations.kt
View File

@ -32,7 +32,7 @@ public object Transformations {
/**
* Create a virtual buffer on top of array
*/
private fun Array<org.apache.commons.math3.complex.Complex>.asBuffer() = VirtualBuffer<Complex>(size) {
private fun Array<org.apache.commons.math3.complex.Complex>.asBuffer() = VirtualBuffer(size) {
val value = get(it)
Complex(value.real, value.imaginary)
}

4
kmath-commons/src/test/kotlin/space/kscience/kmath/commons/expressions/DerivativeStructureExpressionTest.kt
View File

@ -16,7 +16,7 @@ internal inline fun diff(
order: Int,
vararg parameters: Pair<Symbol, Double>,
block: DerivativeStructureField.() -> Unit,
): Unit {
) {
contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
DerivativeStructureField(order, mapOf(*parameters)).run(block)
}
@ -34,7 +34,7 @@ internal class AutoDiffTest {
println(z.derivative(x))
println(z.derivative(y, x))
assertEquals(z.derivative(x, y), z.derivative(y, x))
//check that improper order cause failure
// check improper order cause failure
assertFails { z.derivative(x, x, y) }
}
}

26
kmath-commons/src/test/kotlin/space/kscience/kmath/commons/optimization/OptimizeTest.kt
View File

@ -11,9 +11,12 @@ import space.kscience.kmath.commons.expressions.DerivativeStructureExpression
import space.kscience.kmath.distributions.NormalDistribution
import space.kscience.kmath.expressions.Symbol.Companion.x
import space.kscience.kmath.expressions.Symbol.Companion.y
import space.kscience.kmath.expressions.chiSquaredExpression
import space.kscience.kmath.expressions.symbol
import space.kscience.kmath.optimization.*
import space.kscience.kmath.stat.RandomGenerator
import space.kscience.kmath.structures.asBuffer
import space.kscience.kmath.structures.map
import kotlin.math.pow
import kotlin.test.Test
@ -49,26 +52,27 @@ internal class OptimizeTest {
val sigma = 1.0
val generator = NormalDistribution(0.0, sigma)
val chain = generator.sample(RandomGenerator.default(112667))
val x = (1..100).map(Int::toDouble)
val x = (1..100).map(Int::toDouble).asBuffer()
val y = x.map {
it.pow(2) + it + 1 + chain.next()
}
val yErr = List(x.size) { sigma }
val yErr = List(x.size) { sigma }.asBuffer()
val model = DSProcessor.differentiate { x1 ->
val chi2 = DSProcessor.chiSquaredExpression(
x, y, yErr
) { arg ->
val cWithDefault = bindSymbolOrNull(c) ?: one
bindSymbol(a) * x1.pow(2) + bindSymbol(b) * x1 + cWithDefault
bindSymbol(a) * arg.pow(2) + bindSymbol(b) * arg + cWithDefault
}
val chi2 = FunctionOptimization.chiSquared(x, y, yErr) { x1 ->
val cWithDefault = bindSymbolOrNull(c) ?: one
bindSymbol(a) * x1.pow(2) + bindSymbol(b) * x1 + cWithDefault
}
val result = chi2.minimize(a to 1.5, b to 0.9, c to 1.0)
val result: FunctionOptimization<Double> = chi2.optimizeWith(
CMOptimizer,
mapOf(a to 1.5, b to 0.9, c to 1.0),
FunctionOptimizationTarget.MINIMIZE
)
println(result)
println("Chi2/dof = ${result.value / (x.size - 3)}")
println("Chi2/dof = ${result.resultValue / (x.size - 3)}")
}
}

6
kmath-complex/README.md
View File

@ -8,7 +8,7 @@ Complex and hypercomplex number systems in KMath.
## Artifact:
The Maven coordinates of this project are `space.kscience:kmath-complex:0.3.0-dev-11`.
The Maven coordinates of this project are `space.kscience:kmath-complex:0.3.0-dev-14`.
**Gradle:**
```gradle
@ -18,7 +18,7 @@ repositories {
}
dependencies {
implementation 'space.kscience:kmath-complex:0.3.0-dev-11'
implementation 'space.kscience:kmath-complex:0.3.0-dev-14'
}
```
**Gradle Kotlin DSL:**
@ -29,6 +29,6 @@ repositories {
}
dependencies {
implementation("space.kscience:kmath-complex:0.3.0-dev-11")
implementation("space.kscience:kmath-complex:0.3.0-dev-14")
}
```

42
kmath-complex/src/commonMain/kotlin/space/kscience/kmath/complex/Complex.kt
View File

@ -54,8 +54,8 @@ private val PI_DIV_2 = Complex(PI / 2, 0)
@OptIn(UnstableKMathAPI::class)
public object ComplexField : ExtendedField<Complex>, Norm<Complex, Complex>, NumbersAddOperations<Complex>,
ScaleOperations<Complex> {
public override val zero: Complex = 0.0.toComplex()
public override val one: Complex = 1.0.toComplex()
override val zero: Complex = 0.0.toComplex()
override val one: Complex = 1.0.toComplex()
/**
* The imaginary unit.
@ -68,13 +68,13 @@ public object ComplexField : ExtendedField<Complex>, Norm<Complex, Complex>, Num
override fun scale(a: Complex, value: Double): Complex = Complex(a.re * value, a.im * value)
public override fun add(a: Complex, b: Complex): Complex = Complex(a.re + b.re, a.im + b.im)
// public override fun multiply(a: Complex, k: Number): Complex = Complex(a.re * k.toDouble(), a.im * k.toDouble())
override fun add(a: Complex, b: Complex): Complex = Complex(a.re + b.re, a.im + b.im)
// override fun multiply(a: Complex, k: Number): Complex = Complex(a.re * k.toDouble(), a.im * k.toDouble())
public override fun multiply(a: Complex, b: Complex): Complex =
override fun multiply(a: Complex, b: Complex): Complex =
Complex(a.re * b.re - a.im * b.im, a.re * b.im + a.im * b.re)
public override fun divide(a: Complex, b: Complex): Complex = when {
override fun divide(a: Complex, b: Complex): Complex = when {
abs(b.im) < abs(b.re) -> {
val wr = b.im / b.re
val wd = b.re + wr * b.im
@ -100,31 +100,31 @@ public object ComplexField : ExtendedField<Complex>, Norm<Complex, Complex>, Num
override operator fun Complex.div(k: Number): Complex = Complex(re / k.toDouble(), im / k.toDouble())
public override fun sin(arg: Complex): Complex = i * (exp(-i * arg) - exp(i * arg)) / 2.0
public override fun cos(arg: Complex): Complex = (exp(-i * arg) + exp(i * arg)) / 2.0
override fun sin(arg: Complex): Complex = i * (exp(-i * arg) - exp(i * arg)) / 2.0
override fun cos(arg: Complex): Complex = (exp(-i * arg) + exp(i * arg)) / 2.0
public override fun tan(arg: Complex): Complex {
override fun tan(arg: Complex): Complex {
val e1 = exp(-i * arg)
val e2 = exp(i * arg)
return i * (e1 - e2) / (e1 + e2)
}
public override fun asin(arg: Complex): Complex = -i * ln(sqrt(1 - (arg * arg)) + i * arg)
public override fun acos(arg: Complex): Complex = PI_DIV_2 + i * ln(sqrt(1 - (arg * arg)) + i * arg)
override fun asin(arg: Complex): Complex = -i * ln(sqrt(1 - (arg * arg)) + i * arg)
override fun acos(arg: Complex): Complex = PI_DIV_2 + i * ln(sqrt(1 - (arg * arg)) + i * arg)
public override fun atan(arg: Complex): Complex {
override fun atan(arg: Complex): Complex {
val iArg = i * arg
return i * (ln(1 - iArg) - ln(1 + iArg)) / 2
}
public override fun power(arg: Complex, pow: Number): Complex = if (arg.im == 0.0)
override fun power(arg: Complex, pow: Number): Complex = if (arg.im == 0.0)
arg.re.pow(pow.toDouble()).toComplex()
else
exp(pow * ln(arg))
public override fun exp(arg: Complex): Complex = exp(arg.re) * (cos(arg.im) + i * sin(arg.im))
override fun exp(arg: Complex): Complex = exp(arg.re) * (cos(arg.im) + i * sin(arg.im))
public override fun ln(arg: Complex): Complex = ln(arg.r) + i * atan2(arg.im, arg.re)
override fun ln(arg: Complex): Complex = ln(arg.r) + i * atan2(arg.im, arg.re)
/**
* Adds complex number to real one.
@ -171,9 +171,9 @@ public object ComplexField : ExtendedField<Complex>, Norm<Complex, Complex>, Num
*/
public operator fun Double.times(c: Complex): Complex = Complex(c.re * this, c.im * this)
public override fun norm(arg: Complex): Complex = sqrt(arg.conjugate * arg)
override fun norm(arg: Complex): Complex = sqrt(arg.conjugate * arg)
public override fun bindSymbolOrNull(value: String): Complex? = if (value == "i") i else null
override fun bindSymbolOrNull(value: String): Complex? = if (value == "i") i else null
}
/**
@ -187,16 +187,16 @@ public data class Complex(val re: Double, val im: Double) {
public constructor(re: Number, im: Number) : this(re.toDouble(), im.toDouble())
public constructor(re: Number) : this(re.toDouble(), 0.0)
public override fun toString(): String = "($re + i * $im)"
override fun toString(): String = "($re + i * $im)"
public companion object : MemorySpec<Complex> {
public override val objectSize: Int
override val objectSize: Int
get() = 16
public override fun MemoryReader.read(offset: Int): Complex =
override fun MemoryReader.read(offset: Int): Complex =
Complex(readDouble(offset), readDouble(offset + 8))
public override fun MemoryWriter.write(offset: Int, value: Complex) {
override fun MemoryWriter.write(offset: Int, value: Complex) {
writeDouble(offset, value.re)
writeDouble(offset + 8, value.im)
}

36
kmath-complex/src/commonMain/kotlin/space/kscience/kmath/complex/ComplexFieldND.kt
View File

@ -27,10 +27,10 @@ public class ComplexFieldND(
NumbersAddOperations<StructureND<Complex>>,
ExtendedField<StructureND<Complex>> {
public override val zero: BufferND<Complex> by lazy { produce { zero } }
public override val one: BufferND<Complex> by lazy { produce { one } }
override val zero: BufferND<Complex> by lazy { produce { zero } }
override val one: BufferND<Complex> by lazy { produce { one } }
public override fun number(value: Number): BufferND<Complex> {
override fun number(value: Number): BufferND<Complex> {
val d = value.toComplex() // minimize conversions
return produce { d }
}
@ -81,25 +81,25 @@ public class ComplexFieldND(
// return BufferedNDFieldElement(this, buffer)
// }
public override fun power(arg: StructureND<Complex>, pow: Number): BufferND<Complex> = arg.map { power(it, pow) }
override fun power(arg: StructureND<Complex>, pow: Number): BufferND<Complex> = arg.map { power(it, pow) }
public override fun exp(arg: StructureND<Complex>): BufferND<Complex> = arg.map { exp(it) }
override fun exp(arg: StructureND<Complex>): BufferND<Complex> = arg.map { exp(it) }
public override fun ln(arg: StructureND<Complex>): BufferND<Complex> = arg.map { ln(it) }
override fun ln(arg: StructureND<Complex>): BufferND<Complex> = arg.map { ln(it) }
public override fun sin(arg: StructureND<Complex>): BufferND<Complex> = arg.map { sin(it) }
public override fun cos(arg: StructureND<Complex>): BufferND<Complex> = arg.map { cos(it) }
public override fun tan(arg: StructureND<Complex>): BufferND<Complex> = arg.map { tan(it) }
public override fun asin(arg: StructureND<Complex>): BufferND<Complex> = arg.map { asin(it) }
public override fun acos(arg: StructureND<Complex>): BufferND<Complex> = arg.map { acos(it) }
public override fun atan(arg: StructureND<Complex>): BufferND<Complex> = arg.map { atan(it) }
override fun sin(arg: StructureND<Complex>): BufferND<Complex> = arg.map { sin(it) }
override fun cos(arg: StructureND<Complex>): BufferND<Complex> = arg.map { cos(it) }
override fun tan(arg: StructureND<Complex>): BufferND<Complex> = arg.map { tan(it) }
override fun asin(arg: StructureND<Complex>): BufferND<Complex> = arg.map { asin(it) }
override fun acos(arg: StructureND<Complex>): BufferND<Complex> = arg.map { acos(it) }
override fun atan(arg: StructureND<Complex>): BufferND<Complex> = arg.map { atan(it) }
public override fun sinh(arg: StructureND<Complex>): BufferND<Complex> = arg.map { sinh(it) }
public override fun cosh(arg: StructureND<Complex>): BufferND<Complex> = arg.map { cosh(it) }
public override fun tanh(arg: StructureND<Complex>): BufferND<Complex> = arg.map { tanh(it) }
public override fun asinh(arg: StructureND<Complex>): BufferND<Complex> = arg.map { asinh(it) }
public override fun acosh(arg: StructureND<Complex>): BufferND<Complex> = arg.map { acosh(it) }
public override fun atanh(arg: StructureND<Complex>): BufferND<Complex> = arg.map { atanh(it) }
override fun sinh(arg: StructureND<Complex>): BufferND<Complex> = arg.map { sinh(it) }
override fun cosh(arg: StructureND<Complex>): BufferND<Complex> = arg.map { cosh(it) }
override fun tanh(arg: StructureND<Complex>): BufferND<Complex> = arg.map { tanh(it) }
override fun asinh(arg: StructureND<Complex>): BufferND<Complex> = arg.map { asinh(it) }
override fun acosh(arg: StructureND<Complex>): BufferND<Complex> = arg.map { acosh(it) }
override fun atanh(arg: StructureND<Complex>): BufferND<Complex> = arg.map { atanh(it) }
}

52
kmath-complex/src/commonMain/kotlin/space/kscience/kmath/complex/Quaternion.kt
View File

@ -63,20 +63,20 @@ public object QuaternionField : Field<Quaternion>, Norm<Quaternion, Quaternion>,
*/
public val k: Quaternion = Quaternion(0, 0, 0, 1)
public override fun add(a: Quaternion, b: Quaternion): Quaternion =
override fun add(a: Quaternion, b: Quaternion): Quaternion =
Quaternion(a.w + b.w, a.x + b.x, a.y + b.y, a.z + b.z)
public override fun scale(a: Quaternion, value: Double): Quaternion =
override fun scale(a: Quaternion, value: Double): Quaternion =
Quaternion(a.w * value, a.x * value, a.y * value, a.z * value)
public override fun multiply(a: Quaternion, b: Quaternion): Quaternion = Quaternion(
override fun multiply(a: Quaternion, b: Quaternion): Quaternion = Quaternion(
a.w * b.w - a.x * b.x - a.y * b.y - a.z * b.z,
a.w * b.x + a.x * b.w + a.y * b.z - a.z * b.y,
a.w * b.y - a.x * b.z + a.y * b.w + a.z * b.x,
a.w * b.z + a.x * b.y - a.y * b.x + a.z * b.w,
)
public override fun divide(a: Quaternion, b: Quaternion): Quaternion {
override fun divide(a: Quaternion, b: Quaternion): Quaternion {
val s = b.w * b.w + b.x * b.x + b.y * b.y + b.z * b.z
return Quaternion(
@ -87,7 +87,7 @@ public object QuaternionField : Field<Quaternion>, Norm<Quaternion, Quaternion>,
)
}
public override fun power(arg: Quaternion, pow: Number): Quaternion {
override fun power(arg: Quaternion, pow: Number): Quaternion {
if (pow is Int) return pwr(arg, pow)
if (floor(pow.toDouble()) == pow.toDouble()) return pwr(arg, pow.toInt())
return exp(pow * ln(arg))
@ -131,7 +131,7 @@ public object QuaternionField : Field<Quaternion>, Norm<Quaternion, Quaternion>,
return Quaternion(a2 * a2 - 6 * a2 * n1 + n1 * n1, x.x * n2, x.y * n2, x.z * n2)
}
public override fun exp(arg: Quaternion): Quaternion {
override fun exp(arg: Quaternion): Quaternion {
val un = arg.x * arg.x + arg.y * arg.y + arg.z * arg.z
if (un == 0.0) return exp(arg.w).toQuaternion()
val n1 = sqrt(un)
@ -140,14 +140,14 @@ public object QuaternionField : Field<Quaternion>, Norm<Quaternion, Quaternion>,
return Quaternion(ea * cos(n1), n2 * arg.x, n2 * arg.y, n2 * arg.z)
}
public override fun ln(arg: Quaternion): Quaternion {
override fun ln(arg: Quaternion): Quaternion {
val nu2 = arg.x * arg.x + arg.y * arg.y + arg.z * arg.z
if (nu2 == 0.0)
return if (arg.w > 0)
Quaternion(ln(arg.w), 0, 0, 0)
else {
val l = ComplexField { ComplexField.ln(arg.w.toComplex()) }
val l = ComplexField { ln(arg.w.toComplex()) }
Quaternion(l.re, l.im, 0, 0)
}
@ -158,21 +158,21 @@ public object QuaternionField : Field<Quaternion>, Norm<Quaternion, Quaternion>,
return Quaternion(ln(n), th * arg.x, th * arg.y, th * arg.z)
}
public override operator fun Number.plus(b: Quaternion): Quaternion = Quaternion(toDouble() + b.w, b.x, b.y, b.z)
override operator fun Number.plus(b: Quaternion): Quaternion = Quaternion(toDouble() + b.w, b.x, b.y, b.z)
public override operator fun Number.minus(b: Quaternion): Quaternion =
override operator fun Number.minus(b: Quaternion): Quaternion =
Quaternion(toDouble() - b.w, -b.x, -b.y, -b.z)
public override operator fun Quaternion.plus(b: Number): Quaternion = Quaternion(w + b.toDouble(), x, y, z)
public override operator fun Quaternion.minus(b: Number): Quaternion = Quaternion(w - b.toDouble(), x, y, z)
override operator fun Quaternion.plus(b: Number): Quaternion = Quaternion(w + b.toDouble(), x, y, z)
override operator fun Quaternion.minus(b: Number): Quaternion = Quaternion(w - b.toDouble(), x, y, z)
public override operator fun Number.times(b: Quaternion): Quaternion =
override operator fun Number.times(b: Quaternion): Quaternion =
Quaternion(toDouble() * b.w, toDouble() * b.x, toDouble() * b.y, toDouble() * b.z)
public override fun Quaternion.unaryMinus(): Quaternion = Quaternion(-w, -x, -y, -z)
public override fun norm(arg: Quaternion): Quaternion = sqrt(arg.conjugate * arg)
override fun Quaternion.unaryMinus(): Quaternion = Quaternion(-w, -x, -y, -z)
override fun norm(arg: Quaternion): Quaternion = sqrt(arg.conjugate * arg)
public override fun bindSymbolOrNull(value: String): Quaternion? = when (value) {
override fun bindSymbolOrNull(value: String): Quaternion? = when (value) {
"i" -> i
"j" -> j
"k" -> k
@ -181,12 +181,12 @@ public object QuaternionField : Field<Quaternion>, Norm<Quaternion, Quaternion>,
override fun number(value: Number): Quaternion = value.toQuaternion()
public override fun sinh(arg: Quaternion): Quaternion = (exp(arg) - exp(-arg)) / 2.0
public override fun cosh(arg: Quaternion): Quaternion = (exp(arg) + exp(-arg)) / 2.0
public override fun tanh(arg: Quaternion): Quaternion = (exp(arg) - exp(-arg)) / (exp(-arg) + exp(arg))
public override fun asinh(arg: Quaternion): Quaternion = ln(sqrt(arg * arg + one) + arg)
public override fun acosh(arg: Quaternion): Quaternion = ln(arg + sqrt((arg - one) * (arg + one)))
public override fun atanh(arg: Quaternion): Quaternion = (ln(arg + one) - ln(one - arg)) / 2.0
override fun sinh(arg: Quaternion): Quaternion = (exp(arg) - exp(-arg)) / 2.0
override fun cosh(arg: Quaternion): Quaternion = (exp(arg) + exp(-arg)) / 2.0
override fun tanh(arg: Quaternion): Quaternion = (exp(arg) - exp(-arg)) / (exp(-arg) + exp(arg))
override fun asinh(arg: Quaternion): Quaternion = ln(sqrt(arg * arg + one) + arg)
override fun acosh(arg: Quaternion): Quaternion = ln(arg + sqrt((arg - one) * (arg + one)))
override fun atanh(arg: Quaternion): Quaternion = (ln(arg + one) - ln(one - arg)) / 2.0
}
/**
@ -224,16 +224,16 @@ public data class Quaternion(
/**
* Returns a string representation of this quaternion.
*/
public override fun toString(): String = "($w + $x * i + $y * j + $z * k)"
override fun toString(): String = "($w + $x * i + $y * j + $z * k)"
public companion object : MemorySpec<Quaternion> {
public override val objectSize: Int
override val objectSize: Int
get() = 32
public override fun MemoryReader.read(offset: Int): Quaternion =
override fun MemoryReader.read(offset: Int): Quaternion =
Quaternion(readDouble(offset), readDouble(offset + 8), readDouble(offset + 16), readDouble(offset + 24))
public override fun MemoryWriter.write(offset: Int, value: Quaternion) {
override fun MemoryWriter.write(offset: Int, value: Quaternion) {
writeDouble(offset, value.w)
writeDouble(offset + 8, value.x)
writeDouble(offset + 16, value.y)

8
kmath-core/README.md
View File

@ -10,12 +10,12 @@ The core interfaces of KMath.
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](src/commonMain/kotlin/space/kscience/kmath/domains) : Domains
- [autodif](src/commonMain/kotlin/space/kscience/kmath/expressions/SimpleAutoDiff.kt) : Automatic differentiation
- [autodiff](src/commonMain/kotlin/space/kscience/kmath/expressions/SimpleAutoDiff.kt) : Automatic differentiation
## Artifact:
The Maven coordinates of this project are `space.kscience:kmath-core:0.3.0-dev-11`.
The Maven coordinates of this project are `space.kscience:kmath-core:0.3.0-dev-14`.
**Gradle:**
```gradle
@ -25,7 +25,7 @@ repositories {
}
dependencies {
implementation 'space.kscience:kmath-core:0.3.0-dev-11'
implementation 'space.kscience:kmath-core:0.3.0-dev-14'
}
```
**Gradle Kotlin DSL:**
@ -36,6 +36,6 @@ repositories {
}
dependencies {
implementation("space.kscience:kmath-core:0.3.0-dev-11")
implementation("space.kscience:kmath-core:0.3.0-dev-14")
}
```

43
kmath-core/build.gradle.kts
View File

@ -19,51 +19,42 @@ readme {
feature(
id = "algebras",
description = """
Algebraic structures like rings, spaces and fields.
""".trimIndent(),
ref = "src/commonMain/kotlin/space/kscience/kmath/operations/Algebra.kt"
)
ref = "src/commonMain/kotlin/space/kscience/kmath/operations/Algebra.kt",
) { "Algebraic structures like rings, spaces and fields." }
feature(
id = "nd",
description = "Many-dimensional structures and operations on them.",
ref = "src/commonMain/kotlin/space/kscience/kmath/structures/StructureND.kt"
)
ref = "src/commonMain/kotlin/space/kscience/kmath/structures/StructureND.kt",
) { "Many-dimensional structures and operations on them." }
feature(
id = "linear",
description = """
Basic linear algebra operations (sums, products, etc.), backed by the `Space` API. Advanced linear algebra operations like matrix inversion and LU decomposition.
""".trimIndent(),
ref = "src/commonMain/kotlin/space/kscience/kmath/operations/Algebra.kt"
)
ref = "src/commonMain/kotlin/space/kscience/kmath/operations/Algebra.kt",
) { "Basic linear algebra operations (sums, products, etc.), backed by the `Space` API. Advanced linear algebra operations like matrix inversion and LU decomposition." }
feature(
id = "buffers",
description = "One-dimensional structure",
ref = "src/commonMain/kotlin/space/kscience/kmath/structures/Buffers.kt"
)
ref = "src/commonMain/kotlin/space/kscience/kmath/structures/Buffers.kt",
) { "One-dimensional structure" }
feature(
id = "expressions",
description = """
ref = "src/commonMain/kotlin/space/kscience/kmath/expressions"
) {
"""
By writing a single mathematical expression once, users will be able to apply different types of
objects to the expression by providing a context. Expressions can be used for a wide variety of purposes from high
performance calculations to code generation.
""".trimIndent(),
ref = "src/commonMain/kotlin/space/kscience/kmath/expressions"
)
""".trimIndent()
}
feature(
id = "domains",
description = "Domains",
ref = "src/commonMain/kotlin/space/kscience/kmath/domains"
)
ref = "src/commonMain/kotlin/space/kscience/kmath/domains",
) { "Domains" }
feature(
id = "autodif",
description = "Automatic differentiation",
id = "autodiff",
ref = "src/commonMain/kotlin/space/kscience/kmath/expressions/SimpleAutoDiff.kt"
)
) { "Automatic differentiation" }
}

2
kmath-core/src/commonMain/kotlin/space/kscience/kmath/data/XYColumnarData.kt
View File

@ -16,7 +16,7 @@ import kotlin.math.max
* The buffer of X values.
*/
@UnstableKMathAPI
public interface XYColumnarData<T, out X : T, out Y : T> : ColumnarData<T> {
public interface XYColumnarData<out T, out X : T, out Y : T> : ColumnarData<T> {
/**
* The buffer of X values
*/

4
kmath-core/src/commonMain/kotlin/space/kscience/kmath/data/XYZColumnarData.kt
View File

@ -14,7 +14,7 @@ import space.kscience.kmath.structures.Buffer
* Inherits [XYColumnarData].
*/
@UnstableKMathAPI
public interface XYZColumnarData<T, out X : T, out Y : T, out Z : T> : XYColumnarData<T, X, Y> {
public interface XYZColumnarData<out T, out X : T, out Y : T, out Z : T> : XYColumnarData<T, X, Y> {
public val z: Buffer<Z>
override fun get(symbol: Symbol): Buffer<T>? = when (symbol) {
@ -23,4 +23,4 @@ public interface XYZColumnarData<T, out X : T, out Y : T, out Z : T> : XYColumna
Symbol.z -> z
else -> null
}
}
}

2
kmath-core/src/commonMain/kotlin/space/kscience/kmath/domains/Domain.kt
View File

@ -12,7 +12,7 @@ import space.kscience.kmath.linear.Point
*
* @param T the type of element of this domain.
*/
public interface Domain<T : Any> {
public interface Domain<in T : Any> {
/**
* Checks if the specified point is contained in this domain.
*/

10
kmath-core/src/commonMain/kotlin/space/kscience/kmath/domains/HyperSquareDomain.kt
View File

@ -17,17 +17,17 @@ import space.kscience.kmath.structures.indices
*/
@UnstableKMathAPI
public class HyperSquareDomain(private val lower: Buffer<Double>, private val upper: Buffer<Double>) : DoubleDomain {
public override val dimension: Int get() = lower.size
override val dimension: Int get() = lower.size
public override operator fun contains(point: Point<Double>): Boolean = point.indices.all { i ->
override operator fun contains(point: Point<Double>): Boolean = point.indices.all { i ->
point[i] in lower[i]..upper[i]
}
public override fun getLowerBound(num: Int): Double = lower[num]
override fun getLowerBound(num: Int): Double = lower[num]
public override fun getUpperBound(num: Int): Double = upper[num]
override fun getUpperBound(num: Int): Double = upper[num]
public override fun volume(): Double {
override fun volume(): Double {
var res = 1.0
for (i in 0 until dimension) {

10
kmath-core/src/commonMain/kotlin/space/kscience/kmath/domains/UnconstrainedDomain.kt
View File

@ -8,12 +8,12 @@ import space.kscience.kmath.linear.Point
import space.kscience.kmath.misc.UnstableKMathAPI
@UnstableKMathAPI
public class UnconstrainedDomain(public override val dimension: Int) : DoubleDomain {
public override operator fun contains(point: Point<Double>): Boolean = true
public class UnconstrainedDomain(override val dimension: Int) : DoubleDomain {
override operator fun contains(point: Point<Double>): Boolean = true
public override fun getLowerBound(num: Int): Double = Double.NEGATIVE_INFINITY
override fun getLowerBound(num: Int): Double = Double.NEGATIVE_INFINITY
public override fun getUpperBound(num: Int): Double = Double.POSITIVE_INFINITY
override fun getUpperBound(num: Int): Double = Double.POSITIVE_INFINITY
public override fun volume(): Double = Double.POSITIVE_INFINITY
override fun volume(): Double = Double.POSITIVE_INFINITY
}

10
kmath-core/src/commonMain/kotlin/space/kscience/kmath/domains/UnivariateDomain.kt
View File

@ -10,24 +10,24 @@ import space.kscience.kmath.misc.UnstableKMathAPI
@UnstableKMathAPI
public class UnivariateDomain(public val range: ClosedFloatingPointRange<Double>) : DoubleDomain {
public override val dimension: Int get() = 1
override val dimension: Int get() = 1
public operator fun contains(d: Double): Boolean = range.contains(d)
public override operator fun contains(point: Point<Double>): Boolean {
override operator fun contains(point: Point<Double>): Boolean {
require(point.size == 0)
return contains(point[0])
}
public override fun getLowerBound(num: Int): Double {
override fun getLowerBound(num: Int): Double {
require(num == 0)
return range.start
}
public override fun getUpperBound(num: Int): Double {
override fun getUpperBound(num: Int): Double {
require(num == 0)
return range.endInclusive
}
public override fun volume(): Double = range.endInclusive - range.start
override fun volume(): Double = range.endInclusive - range.start
}

19
kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/DifferentiableExpression.kt
View File

@ -8,10 +8,9 @@ package space.kscience.kmath.expressions
import space.kscience.kmath.operations.Algebra
/**
* Represents expression which structure can be differentiated.
* Represents expression, which structure can be differentiated.
*
* @param T the type this expression takes as argument and returns.
* @param R the type of expression this expression can be differentiated to.
*/
public interface DifferentiableExpression<T> : Expression<T> {
/**
@ -26,16 +25,18 @@ public interface DifferentiableExpression<T> : Expression<T> {
public fun <T> DifferentiableExpression<T>.derivative(symbols: List<Symbol>): Expression<T> =
derivativeOrNull(symbols) ?: error("Derivative by symbols $symbols not provided")
public fun <T> DifferentiableExpression<T>.derivative(vararg symbols: Symbol): Expression<T> =
public fun <T> DifferentiableExpression<T>.derivative(vararg symbols: Symbol): Expression<T> =
derivative(symbols.toList())
public fun <T> DifferentiableExpression<T>.derivative(name: String): Expression<T> =
public fun <T> DifferentiableExpression<T>.derivative(name: String): Expression<T> =
derivative(StringSymbol(name))
/**
* A special type of [DifferentiableExpression] which returns typed expressions as derivatives
* A special type of [DifferentiableExpression] which returns typed expressions as derivatives.
*
* @param R the type of expression this expression can be differentiated to.
*/
public interface SpecialDifferentiableExpression<T, R: Expression<T>>: DifferentiableExpression<T> {
public interface SpecialDifferentiableExpression<T, out R : Expression<T>> : DifferentiableExpression<T> {
override fun derivativeOrNull(symbols: List<Symbol>): R?
}
@ -55,9 +56,9 @@ public abstract class FirstDerivativeExpression<T> : DifferentiableExpression<T>
/**
* Returns first derivative of this expression by given [symbol].
*/
public abstract fun derivativeOrNull(symbol: Symbol): Expression<T>?
public abstract fun derivativeOrNull(symbol: Symbol): Expression<T>?
public final override fun derivativeOrNull(symbols: List<Symbol>): Expression<T>? {
public final override fun derivativeOrNull(symbols: List<Symbol>): Expression<T>? {
val dSymbol = symbols.firstOrNull() ?: return null
return derivativeOrNull(dSymbol)
}
@ -69,6 +70,6 @@ public abstract class FirstDerivativeExpression<T> : DifferentiableExpression<T>
* @param I type of the actual expression state
* @param A type of expression algebra
*/
public fun interface AutoDiffProcessor<T, I, A : Algebra<I>> {
public fun interface AutoDiffProcessor<T, I, out A : Algebra<I>> {
public fun differentiate(function: A.() -> I): DifferentiableExpression<T>
}

4
kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/Expression.kt
View File

@ -43,7 +43,7 @@ public operator fun <T> Expression<T>.invoke(vararg pairs: Pair<Symbol, T>): T =
/**
* Calls this expression from arguments.
*
* @param pairs the pairs of arguments' names to values.
* @param pairs the pairs of arguments' names to value.
* @return a value.
*/
@JvmName("callByString")
@ -60,7 +60,7 @@ public operator fun <T> Expression<T>.invoke(vararg pairs: Pair<String, T>): T =
public interface ExpressionAlgebra<in T, E> : Algebra<E> {
/**
* A constant expression which does not depend on arguments
* A constant expression that does not depend on arguments.
*/
public fun const(value: T): E
}

108
kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/FunctionalExpressionAlgebra.kt
View File

@ -6,43 +6,39 @@
package space.kscience.kmath.expressions
import space.kscience.kmath.operations.*
import kotlin.contracts.InvocationKind
import kotlin.contracts.contract
/**
* A context class for [Expression] construction.
*
* @param algebra The algebra to provide for Expressions built.
*/
public abstract class FunctionalExpressionAlgebra<T, A : Algebra<T>>(
public abstract class FunctionalExpressionAlgebra<T, out A : Algebra<T>>(
public val algebra: A,
) : ExpressionAlgebra<T, Expression<T>> {
/**
* Builds an Expression of constant expression which does not depend on arguments.
* Builds an Expression of constant expression that does not depend on arguments.
*/
public override fun const(value: T): Expression<T> = Expression { value }
override fun const(value: T): Expression<T> = Expression { value }
/**
* Builds an Expression to access a variable.
*/
public override fun bindSymbolOrNull(value: String): Expression<T>? = Expression { arguments ->
override fun bindSymbolOrNull(value: String): Expression<T>? = Expression { arguments ->
algebra.bindSymbolOrNull(value)
?: arguments[StringSymbol(value)]
?: error("Symbol '$value' is not supported in $this")
}
/**
* Builds an Expression of dynamic call of binary operation [operation] on [left] and [right].
*/
public override fun binaryOperationFunction(operation: String): (left: Expression<T>, right: Expression<T>) -> Expression<T> =
override fun binaryOperationFunction(operation: String): (left: Expression<T>, right: Expression<T>) -> Expression<T> =
{ left, right ->
Expression { arguments ->
algebra.binaryOperationFunction(operation)(left.invoke(arguments), right.invoke(arguments))
}
}
/**
* Builds an Expression of dynamic call of unary operation with name [operation] on [arg].
*/
public override fun unaryOperationFunction(operation: String): (arg: Expression<T>) -> Expression<T> = { arg ->
override fun unaryOperationFunction(operation: String): (arg: Expression<T>) -> Expression<T> = { arg ->
Expression { arguments -> algebra.unaryOperationFunction(operation)(arg.invoke(arguments)) }
}
}
@ -50,24 +46,24 @@ public abstract class FunctionalExpressionAlgebra<T, A : Algebra<T>>(
/**
* A context class for [Expression] construction for [Ring] algebras.
*/
public open class FunctionalExpressionGroup<T, A : Group<T>>(
public open class FunctionalExpressionGroup<T, out A : Group<T>>(
algebra: A,
) : FunctionalExpressionAlgebra<T, A>(algebra), Group<Expression<T>> {
public override val zero: Expression<T> get() = const(algebra.zero)
override val zero: Expression<T> get() = const(algebra.zero)
public override fun Expression<T>.unaryMinus(): Expression<T> =
override fun Expression<T>.unaryMinus(): Expression<T> =
unaryOperation(GroupOperations.MINUS_OPERATION, this)
/**
* Builds an Expression of addition of two another expressions.
*/
public override fun add(a: Expression<T>, b: Expression<T>): Expression<T> =
override fun add(a: Expression<T>, b: Expression<T>): Expression<T> =
binaryOperation(GroupOperations.PLUS_OPERATION, a, b)
// /**
// * Builds an Expression of multiplication of expression by number.
// */
// public override fun multiply(a: Expression<T>, k: Number): Expression<T> = Expression { arguments ->
// override fun multiply(a: Expression<T>, k: Number): Expression<T> = Expression { arguments ->
// algebra.multiply(a.invoke(arguments), k)
// }
@ -76,110 +72,122 @@ public open class FunctionalExpressionGroup<T, A : Group<T>>(
public operator fun T.plus(arg: Expression<T>): Expression<T> = arg + this
public operator fun T.minus(arg: Expression<T>): Expression<T> = arg - this
public override fun unaryOperationFunction(operation: String): (arg: Expression<T>) -> Expression<T> =
override fun unaryOperationFunction(operation: String): (arg: Expression<T>) -> Expression<T> =
super<FunctionalExpressionAlgebra>.unaryOperationFunction(operation)
public override fun binaryOperationFunction(operation: String): (left: Expression<T>, right: Expression<T>) -> Expression<T> =
override fun binaryOperationFunction(operation: String): (left: Expression<T>, right: Expression<T>) -> Expression<T> =
super<FunctionalExpressionAlgebra>.binaryOperationFunction(operation)
}
public open class FunctionalExpressionRing<T, A : Ring<T>>(
public open class FunctionalExpressionRing<T, out A : Ring<T>>(
algebra: A,
) : FunctionalExpressionGroup<T, A>(algebra), Ring<Expression<T>> {
public override val one: Expression<T> get() = const(algebra.one)
override val one: Expression<T> get() = const(algebra.one)
/**
* Builds an Expression of multiplication of two expressions.
*/
public override fun multiply(a: Expression<T>, b: Expression<T>): Expression<T> =
override fun multiply(a: Expression<T>, b: Expression<T>): Expression<T> =
binaryOperationFunction(RingOperations.TIMES_OPERATION)(a, b)
public operator fun Expression<T>.times(arg: T): Expression<T> = this * const(arg)
public operator fun T.times(arg: Expression<T>): Expression<T> = arg * this
public override fun unaryOperationFunction(operation: String): (arg: Expression<T>) -> Expression<T> =
override fun unaryOperationFunction(operation: String): (arg: Expression<T>) -> Expression<T> =
super<FunctionalExpressionGroup>.unaryOperationFunction(operation)
public override fun binaryOperationFunction(operation: String): (left: Expression<T>, right: Expression<T>) -> Expression<T> =
override fun binaryOperationFunction(operation: String): (left: Expression<T>, right: Expression<T>) -> Expression<T> =
super<FunctionalExpressionGroup>.binaryOperationFunction(operation)
}
public open class FunctionalExpressionField<T, A : Field<T>>(
public open class FunctionalExpressionField<T, out A : Field<T>>(
algebra: A,
) : FunctionalExpressionRing<T, A>(algebra), Field<Expression<T>>, ScaleOperations<Expression<T>> {
/**
* Builds an Expression of division an expression by another one.
*/
public override fun divide(a: Expression<T>, b: Expression<T>): Expression<T> =
override fun divide(a: Expression<T>, b: Expression<T>): Expression<T> =
binaryOperationFunction(FieldOperations.DIV_OPERATION)(a, b)
public operator fun Expression<T>.div(arg: T): Expression<T> = this / const(arg)
public operator fun T.div(arg: Expression<T>): Expression<T> = arg / this
public override fun unaryOperationFunction(operation: String): (arg: Expression<T>) -> Expression<T> =
override fun unaryOperationFunction(operation: String): (arg: Expression<T>) -> Expression<T> =
super<FunctionalExpressionRing>.unaryOperationFunction(operation)
public override fun binaryOperationFunction(operation: String): (left: Expression<T>, right: Expression<T>) -> Expression<T> =
override fun binaryOperationFunction(operation: String): (left: Expression<T>, right: Expression<T>) -> Expression<T> =
super<FunctionalExpressionRing>.binaryOperationFunction(operation)
public override fun scale(a: Expression<T>, value: Double): Expression<T> = algebra {
override fun scale(a: Expression<T>, value: Double): Expression<T> = algebra {
Expression { args -> a(args) * value }
}
public override fun bindSymbolOrNull(value: String): Expression<T>? =
override fun bindSymbolOrNull(value: String): Expression<T>? =
super<FunctionalExpressionRing>.bindSymbolOrNull(value)
}
public open class FunctionalExpressionExtendedField<T, A : ExtendedField<T>>(
public open class FunctionalExpressionExtendedField<T, out A : ExtendedField<T>>(
algebra: A,
) : FunctionalExpressionField<T, A>(algebra), ExtendedField<Expression<T>> {
public override fun number(value: Number): Expression<T> = const(algebra.number(value))
override fun number(value: Number): Expression<T> = const(algebra.number(value))
public override fun sqrt(arg: Expression<T>): Expression<T> =
override fun sqrt(arg: Expression<T>): Expression<T> =
unaryOperationFunction(PowerOperations.SQRT_OPERATION)(arg)
public override fun sin(arg: Expression<T>): Expression<T> =
override fun sin(arg: Expression<T>): Expression<T> =
unaryOperationFunction(TrigonometricOperations.SIN_OPERATION)(arg)
public override fun cos(arg: Expression<T>): Expression<T> =
override fun cos(arg: Expression<T>): Expression<T> =
unaryOperationFunction(TrigonometricOperations.COS_OPERATION)(arg)
public override fun asin(arg: Expression<T>): Expression<T> =
override fun asin(arg: Expression<T>): Expression<T> =
unaryOperationFunction(TrigonometricOperations.ASIN_OPERATION)(arg)
public override fun acos(arg: Expression<T>): Expression<T> =
override fun acos(arg: Expression<T>): Expression<T> =
unaryOperationFunction(TrigonometricOperations.ACOS_OPERATION)(arg)
public override fun atan(arg: Expression<T>): Expression<T> =
override fun atan(arg: Expression<T>): Expression<T> =
unaryOperationFunction(TrigonometricOperations.ATAN_OPERATION)(arg)
public override fun power(arg: Expression<T>, pow: Number): Expression<T> =
override fun power(arg: Expression<T>, pow: Number): Expression<T> =
binaryOperationFunction(PowerOperations.POW_OPERATION)(arg, number(pow))
public override fun exp(arg: Expression<T>): Expression<T> =
override fun exp(arg: Expression<T>): Expression<T> =
unaryOperationFunction(ExponentialOperations.EXP_OPERATION)(arg)
public override fun ln(arg: Expression<T>): Expression<T> =
override fun ln(arg: Expression<T>): Expression<T> =
unaryOperationFunction(ExponentialOperations.LN_OPERATION)(arg)
public override fun unaryOperationFunction(operation: String): (arg: Expression<T>) -> Expression<T> =
override fun unaryOperationFunction(operation: String): (arg: Expression<T>) -> Expression<T> =
super<FunctionalExpressionField>.unaryOperationFunction(operation)
public override fun binaryOperationFunction(operation: String): (left: Expression<T>, right: Expression<T>) -> Expression<T> =
override fun binaryOperationFunction(operation: String): (left: Expression<T>, right: Expression<T>) -> Expression<T> =
super<FunctionalExpressionField>.binaryOperationFunction(operation)
public override fun bindSymbol(value: String): Expression<T> = super<FunctionalExpressionField>.bindSymbol(value)
override fun bindSymbol(value: String): Expression<T> = super<FunctionalExpressionField>.bindSymbol(value)
}
public inline fun <T, A : Ring<T>> A.expressionInSpace(block: FunctionalExpressionGroup<T, A>.() -> Expression<T>): Expression<T> =
FunctionalExpressionGroup(this).block()
public inline fun <T, A : Group<T>> A.expressionInGroup(
block: FunctionalExpressionGroup<T, A>.() -> Expression<T>,
): Expression<T> {
contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
return FunctionalExpressionGroup(this).block()
}
public inline fun <T, A : Ring<T>> A.expressionInRing(block: FunctionalExpressionRing<T, A>.() -> Expression<T>): Expression<T> =
FunctionalExpressionRing(this).block()
public inline fun <T, A : Ring<T>> A.expressionInRing(
block: FunctionalExpressionRing<T, A>.() -> Expression<T>,
): Expression<T> {
contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
return FunctionalExpressionRing(this).block()
}
public inline fun <T, A : Field<T>> A.expressionInField(block: FunctionalExpressionField<T, A>.() -> Expression<T>): Expression<T> =
FunctionalExpressionField(this).block()
public inline fun <T, A : Field<T>> A.expressionInField(
block: FunctionalExpressionField<T, A>.() -> Expression<T>,
): Expression<T> {
contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
return FunctionalExpressionField(this).block()
}
public inline fun <T, A : ExtendedField<T>> A.expressionInExtendedField(
block: FunctionalExpressionExtendedField<T, A>.() -> Expression<T>,

138
kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/MstAlgebra.kt
View File

@ -12,14 +12,14 @@ import space.kscience.kmath.operations.*
* [Algebra] over [MST] nodes.
*/
public object MstNumericAlgebra : NumericAlgebra<MST> {
public override fun number(value: Number): MST.Numeric = MST.Numeric(value)
public override fun bindSymbolOrNull(value: String): Symbol = StringSymbol(value)
override fun number(value: Number): MST.Numeric = MST.Numeric(value)
override fun bindSymbolOrNull(value: String): Symbol = StringSymbol(value)
override fun bindSymbol(value: String): Symbol = bindSymbolOrNull(value)
public override fun unaryOperationFunction(operation: String): (arg: MST) -> MST.Unary =
override fun unaryOperationFunction(operation: String): (arg: MST) -> MST.Unary =
{ arg -> MST.Unary(operation, arg) }
public override fun binaryOperationFunction(operation: String): (left: MST, right: MST) -> MST.Binary =
override fun binaryOperationFunction(operation: String): (left: MST, right: MST) -> MST.Binary =
{ left, right -> MST.Binary(operation, left, right) }
}
@ -27,27 +27,27 @@ public object MstNumericAlgebra : NumericAlgebra<MST> {
* [Group] over [MST] nodes.
*/
public object MstGroup : Group<MST>, NumericAlgebra<MST>, ScaleOperations<MST> {
public override val zero: MST.Numeric = number(0.0)
override val zero: MST.Numeric = number(0.0)
public override fun number(value: Number): MST.Numeric = MstNumericAlgebra.number(value)
public override fun bindSymbolOrNull(value: String): Symbol = MstNumericAlgebra.bindSymbolOrNull(value)
public override fun add(a: MST, b: MST): MST.Binary = binaryOperationFunction(GroupOperations.PLUS_OPERATION)(a, b)
public override operator fun MST.unaryPlus(): MST.Unary =
override fun number(value: Number): MST.Numeric = MstNumericAlgebra.number(value)
override fun bindSymbolOrNull(value: String): Symbol = MstNumericAlgebra.bindSymbolOrNull(value)
override fun add(a: MST, b: MST): MST.Binary = binaryOperationFunction(GroupOperations.PLUS_OPERATION)(a, b)
override operator fun MST.unaryPlus(): MST.Unary =
unaryOperationFunction(GroupOperations.PLUS_OPERATION)(this)
public override operator fun MST.unaryMinus(): MST.Unary =
override operator fun MST.unaryMinus(): MST.Unary =
unaryOperationFunction(GroupOperations.MINUS_OPERATION)(this)
public override operator fun MST.minus(b: MST): MST.Binary =
override operator fun MST.minus(b: MST): MST.Binary =
binaryOperationFunction(GroupOperations.MINUS_OPERATION)(this, b)
public override fun scale(a: MST, value: Double): MST.Binary =
override fun scale(a: MST, value: Double): MST.Binary =
binaryOperationFunction(RingOperations.TIMES_OPERATION)(a, number(value))
public override fun binaryOperationFunction(operation: String): (left: MST, right: MST) -> MST.Binary =
override fun binaryOperationFunction(operation: String): (left: MST, right: MST) -> MST.Binary =
MstNumericAlgebra.binaryOperationFunction(operation)
public override fun unaryOperationFunction(operation: String): (arg: MST) -> MST.Unary =
override fun unaryOperationFunction(operation: String): (arg: MST) -> MST.Unary =
MstNumericAlgebra.unaryOperationFunction(operation)
}
@ -57,27 +57,27 @@ public object MstGroup : Group<MST>, NumericAlgebra<MST>, ScaleOperations<MST> {
@Suppress("OVERRIDE_BY_INLINE")
@OptIn(UnstableKMathAPI::class)
public object MstRing : Ring<MST>, NumbersAddOperations<MST>, ScaleOperations<MST> {
public override inline val zero: MST.Numeric get() = MstGroup.zero
public override val one: MST.Numeric = number(1.0)
override inline val zero: MST.Numeric get() = MstGroup.zero
override val one: MST.Numeric = number(1.0)
public override fun number(value: Number): MST.Numeric = MstGroup.number(value)
public override fun bindSymbolOrNull(value: String): Symbol = MstNumericAlgebra.bindSymbolOrNull(value)
public override fun add(a: MST, b: MST): MST.Binary = MstGroup.add(a, b)
override fun number(value: Number): MST.Numeric = MstGroup.number(value)
override fun bindSymbolOrNull(value: String): Symbol = MstNumericAlgebra.bindSymbolOrNull(value)
override fun add(a: MST, b: MST): MST.Binary = MstGroup.add(a, b)
public override fun scale(a: MST, value: Double): MST.Binary =
override fun scale(a: MST, value: Double): MST.Binary =
MstGroup.binaryOperationFunction(RingOperations.TIMES_OPERATION)(a, MstGroup.number(value))
public override fun multiply(a: MST, b: MST): MST.Binary =
override fun multiply(a: MST, b: MST): MST.Binary =
binaryOperationFunction(RingOperations.TIMES_OPERATION)(a, b)
public override operator fun MST.unaryPlus(): MST.Unary = MstGroup { +this@unaryPlus }
public override operator fun MST.unaryMinus(): MST.Unary = MstGroup { -this@unaryMinus }
public override operator fun MST.minus(b: MST): MST.Binary = MstGroup { this@minus - b }
override operator fun MST.unaryPlus(): MST.Unary = MstGroup { +this@unaryPlus }
override operator fun MST.unaryMinus(): MST.Unary = MstGroup { -this@unaryMinus }
override operator fun MST.minus(b: MST): MST.Binary = MstGroup { this@minus - b }
public override fun binaryOperationFunction(operation: String): (left: MST, right: MST) -> MST.Binary =
override fun binaryOperationFunction(operation: String): (left: MST, right: MST) -> MST.Binary =
MstGroup.binaryOperationFunction(operation)
public override fun unaryOperationFunction(operation: String): (arg: MST) -> MST.Unary =
override fun unaryOperationFunction(operation: String): (arg: MST) -> MST.Unary =
MstNumericAlgebra.unaryOperationFunction(operation)
}
@ -87,28 +87,28 @@ public object MstRing : Ring<MST>, NumbersAddOperations<MST>, ScaleOperations<MS
@Suppress("OVERRIDE_BY_INLINE")
@OptIn(UnstableKMathAPI::class)
public object MstField : Field<MST>, NumbersAddOperations<MST>, ScaleOperations<MST> {
public override inline val zero: MST.Numeric get() = MstRing.zero
public override inline val one: MST.Numeric get() = MstRing.one
override inline val zero: MST.Numeric get() = MstRing.zero
override inline val one: MST.Numeric get() = MstRing.one
public override fun bindSymbolOrNull(value: String): Symbol = MstNumericAlgebra.bindSymbolOrNull(value)
public override fun number(value: Number): MST.Numeric = MstRing.number(value)
public override fun add(a: MST, b: MST): MST.Binary = MstRing.add(a, b)
override fun bindSymbolOrNull(value: String): Symbol = MstNumericAlgebra.bindSymbolOrNull(value)
override fun number(value: Number): MST.Numeric = MstRing.number(value)
override fun add(a: MST, b: MST): MST.Binary = MstRing.add(a, b)
public override fun scale(a: MST, value: Double): MST.Binary =
override fun scale(a: MST, value: Double): MST.Binary =
MstGroup.binaryOperationFunction(RingOperations.TIMES_OPERATION)(a, MstGroup.number(value))
public override fun multiply(a: MST, b: MST): MST.Binary = MstRing.multiply(a, b)
public override fun divide(a: MST, b: MST): MST.Binary =
override fun multiply(a: MST, b: MST): MST.Binary = MstRing.multiply(a, b)
override fun divide(a: MST, b: MST): MST.Binary =
binaryOperationFunction(FieldOperations.DIV_OPERATION)(a, b)
public override operator fun MST.unaryPlus(): MST.Unary = MstRing { +this@unaryPlus }
public override operator fun MST.unaryMinus(): MST.Unary = MstRing { -this@unaryMinus }
public override operator fun MST.minus(b: MST): MST.Binary = MstRing { this@minus - b }
override operator fun MST.unaryPlus(): MST.Unary = MstRing { +this@unaryPlus }
override operator fun MST.unaryMinus(): MST.Unary = MstRing { -this@unaryMinus }
override operator fun MST.minus(b: MST): MST.Binary = MstRing { this@minus - b }
public override fun binaryOperationFunction(operation: String): (left: MST, right: MST) -> MST.Binary =
override fun binaryOperationFunction(operation: String): (left: MST, right: MST) -> MST.Binary =
MstRing.binaryOperationFunction(operation)
public override fun unaryOperationFunction(operation: String): (arg: MST) -> MST.Unary =
override fun unaryOperationFunction(operation: String): (arg: MST) -> MST.Unary =
MstRing.unaryOperationFunction(operation)
}
@ -117,45 +117,45 @@ public object MstField : Field<MST>, NumbersAddOperations<MST>, ScaleOperations<
*/
@Suppress("OVERRIDE_BY_INLINE")
public object MstExtendedField : ExtendedField<MST>, NumericAlgebra<MST> {
public override inline val zero: MST.Numeric get() = MstField.zero
public override inline val one: MST.Numeric get() = MstField.one
override inline val zero: MST.Numeric get() = MstField.zero
override inline val one: MST.Numeric get() = MstField.one
public override fun bindSymbolOrNull(value: String): Symbol = MstNumericAlgebra.bindSymbolOrNull(value)
public override fun number(value: Number): MST.Numeric = MstRing.number(value)
public override fun sin(arg: MST): MST.Unary = unaryOperationFunction(TrigonometricOperations.SIN_OPERATION)(arg)
public override fun cos(arg: MST): MST.Unary = unaryOperationFunction(TrigonometricOperations.COS_OPERATION)(arg)
public override fun tan(arg: MST): MST.Unary = unaryOperationFunction(TrigonometricOperations.TAN_OPERATION)(arg)
public override fun asin(arg: MST): MST.Unary = unaryOperationFunction(TrigonometricOperations.ASIN_OPERATION)(arg)
public override fun acos(arg: MST): MST.Unary = unaryOperationFunction(TrigonometricOperations.ACOS_OPERATION)(arg)
public override fun atan(arg: MST): MST.Unary = unaryOperationFunction(TrigonometricOperations.ATAN_OPERATION)(arg)
public override fun sinh(arg: MST): MST.Unary = unaryOperationFunction(ExponentialOperations.SINH_OPERATION)(arg)
public override fun cosh(arg: MST): MST.Unary = unaryOperationFunction(ExponentialOperations.COSH_OPERATION)(arg)
public override fun tanh(arg: MST): MST.Unary = unaryOperationFunction(ExponentialOperations.TANH_OPERATION)(arg)
public override fun asinh(arg: MST): MST.Unary = unaryOperationFunction(ExponentialOperations.ASINH_OPERATION)(arg)
public override fun acosh(arg: MST): MST.Unary = unaryOperationFunction(ExponentialOperations.ACOSH_OPERATION)(arg)
public override fun atanh(arg: MST): MST.Unary = unaryOperationFunction(ExponentialOperations.ATANH_OPERATION)(arg)
public override fun add(a: MST, b: MST): MST.Binary = MstField.add(a, b)
public override fun sqrt(arg: MST): MST = unaryOperationFunction(PowerOperations.SQRT_OPERATION)(arg)
override fun bindSymbolOrNull(value: String): Symbol = MstNumericAlgebra.bindSymbolOrNull(value)
override fun number(value: Number): MST.Numeric = MstRing.number(value)
override fun sin(arg: MST): MST.Unary = unaryOperationFunction(TrigonometricOperations.SIN_OPERATION)(arg)
override fun cos(arg: MST): MST.Unary = unaryOperationFunction(TrigonometricOperations.COS_OPERATION)(arg)
override fun tan(arg: MST): MST.Unary = unaryOperationFunction(TrigonometricOperations.TAN_OPERATION)(arg)
override fun asin(arg: MST): MST.Unary = unaryOperationFunction(TrigonometricOperations.ASIN_OPERATION)(arg)
override fun acos(arg: MST): MST.Unary = unaryOperationFunction(TrigonometricOperations.ACOS_OPERATION)(arg)
override fun atan(arg: MST): MST.Unary = unaryOperationFunction(TrigonometricOperations.ATAN_OPERATION)(arg)
override fun sinh(arg: MST): MST.Unary = unaryOperationFunction(ExponentialOperations.SINH_OPERATION)(arg)
override fun cosh(arg: MST): MST.Unary = unaryOperationFunction(ExponentialOperations.COSH_OPERATION)(arg)
override fun tanh(arg: MST): MST.Unary = unaryOperationFunction(ExponentialOperations.TANH_OPERATION)(arg)
override fun asinh(arg: MST): MST.Unary = unaryOperationFunction(ExponentialOperations.ASINH_OPERATION)(arg)
override fun acosh(arg: MST): MST.Unary = unaryOperationFunction(ExponentialOperations.ACOSH_OPERATION)(arg)
override fun atanh(arg: MST): MST.Unary = unaryOperationFunction(ExponentialOperations.ATANH_OPERATION)(arg)
override fun add(a: MST, b: MST): MST.Binary = MstField.add(a, b)
override fun sqrt(arg: MST): MST = unaryOperationFunction(PowerOperations.SQRT_OPERATION)(arg)
public override fun scale(a: MST, value: Double): MST =
override fun scale(a: MST, value: Double): MST =
binaryOperation(GroupOperations.PLUS_OPERATION, a, number(value))
public override fun multiply(a: MST, b: MST): MST.Binary = MstField.multiply(a, b)
public override fun divide(a: MST, b: MST): MST.Binary = MstField.divide(a, b)
public override operator fun MST.unaryPlus(): MST.Unary = MstField { +this@unaryPlus }
public override operator fun MST.unaryMinus(): MST.Unary = MstField { -this@unaryMinus }
public override operator fun MST.minus(b: MST): MST.Binary = MstField { this@minus - b }
override fun multiply(a: MST, b: MST): MST.Binary = MstField.multiply(a, b)
override fun divide(a: MST, b: MST): MST.Binary = MstField.divide(a, b)
override operator fun MST.unaryPlus(): MST.Unary = MstField { +this@unaryPlus }
override operator fun MST.unaryMinus(): MST.Unary = MstField { -this@unaryMinus }
override operator fun MST.minus(b: MST): MST.Binary = MstField { this@minus - b }
public override fun power(arg: MST, pow: Number): MST.Binary =
override fun power(arg: MST, pow: Number): MST.Binary =
binaryOperationFunction(PowerOperations.POW_OPERATION)(arg, number(pow))
public override fun exp(arg: MST): MST.Unary = unaryOperationFunction(ExponentialOperations.EXP_OPERATION)(arg)
public override fun ln(arg: MST): MST.Unary = unaryOperationFunction(ExponentialOperations.LN_OPERATION)(arg)
override fun exp(arg: MST): MST.Unary = unaryOperationFunction(ExponentialOperations.EXP_OPERATION)(arg)
override fun ln(arg: MST): MST.Unary = unaryOperationFunction(ExponentialOperations.LN_OPERATION)(arg)
public override fun binaryOperationFunction(operation: String): (left: MST, right: MST) -> MST.Binary =
override fun binaryOperationFunction(operation: String): (left: MST, right: MST) -> MST.Binary =
MstField.binaryOperationFunction(operation)
public override fun unaryOperationFunction(operation: String): (arg: MST) -> MST.Unary =
override fun unaryOperationFunction(operation: String): (arg: MST) -> MST.Unary =
MstField.unaryOperationFunction(operation)
}
@ -164,7 +164,7 @@ public object MstExtendedField : ExtendedField<MST>, NumericAlgebra<MST> {
*/
@UnstableKMathAPI
public object MstLogicAlgebra : LogicAlgebra<MST> {
public override fun bindSymbolOrNull(value: String): MST = super.bindSymbolOrNull(value) ?: StringSymbol(value)
override fun bindSymbolOrNull(value: String): MST = super.bindSymbolOrNull(value) ?: StringSymbol(value)
override fun const(boolean: Boolean): Symbol = if (boolean) {
LogicAlgebra.TRUE

73
kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/SimpleAutoDiff.kt
View File

@ -60,8 +60,8 @@ public open class SimpleAutoDiffField<T : Any, F : Field<T>>(
public val context: F,
bindings: Map<Symbol, T>,
) : Field<AutoDiffValue<T>>, ExpressionAlgebra<T, AutoDiffValue<T>>, NumbersAddOperations<AutoDiffValue<T>> {
public override val zero: AutoDiffValue<T> get() = const(context.zero)
public override val one: AutoDiffValue<T> get() = const(context.one)
override val zero: AutoDiffValue<T> get() = const(context.zero)
override val one: AutoDiffValue<T> get() = const(context.one)
// this stack contains pairs of blocks and values to apply them to
private var stack: Array<Any?> = arrayOfNulls<Any?>(8)
@ -119,8 +119,6 @@ public open class SimpleAutoDiffField<T : Any, F : Field<T>>(
get() = getDerivative(this)
set(value) = setDerivative(this, value)
public inline fun const(block: F.() -> T): AutoDiffValue<T> = const(context.block())
/**
* Performs update of derivative after the rest of the formula in the back-pass.
*
@ -151,17 +149,17 @@ public open class SimpleAutoDiffField<T : Any, F : Field<T>>(
// // Overloads for Double constants
//
// public override operator fun Number.plus(b: AutoDiffValue<T>): AutoDiffValue<T> =
// override operator fun Number.plus(b: AutoDiffValue<T>): AutoDiffValue<T> =
// derive(const { this@plus.toDouble() * one + b.value }) { z ->
// b.d += z.d
// }
//
// public override operator fun AutoDiffValue<T>.plus(b: Number): AutoDiffValue<T> = b.plus(this)
// override operator fun AutoDiffValue<T>.plus(b: Number): AutoDiffValue<T> = b.plus(this)
//
// public override operator fun Number.minus(b: AutoDiffValue<T>): AutoDiffValue<T> =
// override operator fun Number.minus(b: AutoDiffValue<T>): AutoDiffValue<T> =
// derive(const { this@minus.toDouble() * one - b.value }) { z -> b.d -= z.d }
//
// public override operator fun AutoDiffValue<T>.minus(b: Number): AutoDiffValue<T> =
// override operator fun AutoDiffValue<T>.minus(b: Number): AutoDiffValue<T> =
// derive(const { this@minus.value - one * b.toDouble() }) { z -> d += z.d }
@ -170,30 +168,35 @@ public open class SimpleAutoDiffField<T : Any, F : Field<T>>(
// Basic math (+, -, *, /)
public override fun add(a: AutoDiffValue<T>, b: AutoDiffValue<T>): AutoDiffValue<T> =
override fun add(a: AutoDiffValue<T>, b: AutoDiffValue<T>): AutoDiffValue<T> =
derive(const { a.value + b.value }) { z ->
a.d += z.d
b.d += z.d
}
public override fun multiply(a: AutoDiffValue<T>, b: AutoDiffValue<T>): AutoDiffValue<T> =
override fun multiply(a: AutoDiffValue<T>, b: AutoDiffValue<T>): AutoDiffValue<T> =
derive(const { a.value * b.value }) { z ->
a.d += z.d * b.value
b.d += z.d * a.value
}
public override fun divide(a: AutoDiffValue<T>, b: AutoDiffValue<T>): AutoDiffValue<T> =
override fun divide(a: AutoDiffValue<T>, b: AutoDiffValue<T>): AutoDiffValue<T> =
derive(const { a.value / b.value }) { z ->
a.d += z.d / b.value
b.d -= z.d * a.value / (b.value * b.value)
}
public override fun scale(a: AutoDiffValue<T>, value: Double): AutoDiffValue<T> =
override fun scale(a: AutoDiffValue<T>, value: Double): AutoDiffValue<T> =
derive(const { value * a.value }) { z ->
a.d += z.d * value
}
}
public inline fun <T : Any, F : Field<T>> SimpleAutoDiffField<T, F>.const(block: F.() -> T): AutoDiffValue<T> {
contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
return const(context.block())
}
/**
* Runs differentiation and establishes [SimpleAutoDiffField] context inside the block of code.
@ -208,7 +211,7 @@ public open class SimpleAutoDiffField<T : Any, F : Field<T>>(
* assertEquals(9.0, x.d) // dy/dx
* ```
*
* @param body the action in [SimpleAutoDiffField] context returning [AutoDiffVariable] to differentiate with respect to.
* @param body the action in [SimpleAutoDiffField] context returning [AutoDiffValue] to differentiate with respect to.
* @return the result of differentiation.
*/
public fun <T : Any, F : Field<T>> F.simpleAutoDiff(
@ -233,12 +236,12 @@ public class SimpleAutoDiffExpression<T : Any, F : Field<T>>(
public val field: F,
public val function: SimpleAutoDiffField<T, F>.() -> AutoDiffValue<T>,
) : FirstDerivativeExpression<T>() {
public override operator fun invoke(arguments: Map<Symbol, T>): T {
override operator fun invoke(arguments: Map<Symbol, T>): T {
//val bindings = arguments.entries.map { it.key.bind(it.value) }
return SimpleAutoDiffField(field, arguments).function().value
}
public override fun derivativeOrNull(symbol: Symbol): Expression<T> = Expression { arguments ->
override fun derivativeOrNull(symbol: Symbol): Expression<T> = Expression { arguments ->
//val bindings = arguments.entries.map { it.key.bind(it.value) }
val derivationResult = SimpleAutoDiffField(field, arguments).differentiate(function)
derivationResult.derivative(symbol)
@ -248,7 +251,9 @@ public class SimpleAutoDiffExpression<T : Any, F : Field<T>>(
/**
* Generate [AutoDiffProcessor] for [SimpleAutoDiffExpression]
*/
public fun <T : Any, F : Field<T>> simpleAutoDiff(field: F): AutoDiffProcessor<T, AutoDiffValue<T>, SimpleAutoDiffField<T, F>, Expression<T>> =
public fun <T : Any, F : Field<T>> simpleAutoDiff(
field: F
): AutoDiffProcessor<T, AutoDiffValue<T>, SimpleAutoDiffField<T, F>> =
AutoDiffProcessor { function ->
SimpleAutoDiffExpression(field, function)
}
@ -343,28 +348,28 @@ public class SimpleAutoDiffExtendedField<T : Any, F : ExtendedField<T>>(
override fun bindSymbol(value: String): AutoDiffValue<T> = super<SimpleAutoDiffField>.bindSymbol(value)
public override fun number(value: Number): AutoDiffValue<T> = const { number(value) }
override fun number(value: Number): AutoDiffValue<T> = const { number(value) }
public override fun scale(a: AutoDiffValue<T>, value: Double): AutoDiffValue<T> = a * number(value)
override fun scale(a: AutoDiffValue<T>, value: Double): AutoDiffValue<T> = a * number(value)
// x ^ 2
public fun sqr(x: AutoDiffValue<T>): AutoDiffValue<T> =
(this as SimpleAutoDiffField<T, F>).sqr(x)
// x ^ 1/2
public override fun sqrt(arg: AutoDiffValue<T>): AutoDiffValue<T> =
override fun sqrt(arg: AutoDiffValue<T>): AutoDiffValue<T> =
(this as SimpleAutoDiffField<T, F>).sqrt(arg)
// x ^ y (const)
public override fun power(arg: AutoDiffValue<T>, pow: Number): AutoDiffValue<T> =
override fun power(arg: AutoDiffValue<T>, pow: Number): AutoDiffValue<T> =
(this as SimpleAutoDiffField<T, F>).pow(arg, pow.toDouble())
// exp(x)
public override fun exp(arg: AutoDiffValue<T>): AutoDiffValue<T> =
override fun exp(arg: AutoDiffValue<T>): AutoDiffValue<T> =
(this as SimpleAutoDiffField<T, F>).exp(arg)
// ln(x)
public override fun ln(arg: AutoDiffValue<T>): AutoDiffValue<T> =
override fun ln(arg: AutoDiffValue<T>): AutoDiffValue<T> =
(this as SimpleAutoDiffField<T, F>).ln(arg)
// x ^ y (any)
@ -374,40 +379,40 @@ public class SimpleAutoDiffExtendedField<T : Any, F : ExtendedField<T>>(
): AutoDiffValue<T> = exp(y * ln(x))
// sin(x)
public override fun sin(arg: AutoDiffValue<T>): AutoDiffValue<T> =
override fun sin(arg: AutoDiffValue<T>): AutoDiffValue<T> =
(this as SimpleAutoDiffField<T, F>).sin(arg)
// cos(x)
public override fun cos(arg: AutoDiffValue<T>): AutoDiffValue<T> =
override fun cos(arg: AutoDiffValue<T>): AutoDiffValue<T> =
(this as SimpleAutoDiffField<T, F>).cos(arg)
public override fun tan(arg: AutoDiffValue<T>): AutoDiffValue<T> =
override fun tan(arg: AutoDiffValue<T>): AutoDiffValue<T> =
(this as SimpleAutoDiffField<T, F>).tan(arg)
public override fun asin(arg: AutoDiffValue<T>): AutoDiffValue<T> =
override fun asin(arg: AutoDiffValue<T>): AutoDiffValue<T> =
(this as SimpleAutoDiffField<T, F>).asin(arg)
public override fun acos(arg: AutoDiffValue<T>): AutoDiffValue<T> =
override fun acos(arg: AutoDiffValue<T>): AutoDiffValue<T> =
(this as SimpleAutoDiffField<T, F>).acos(arg)
public override fun atan(arg: AutoDiffValue<T>): AutoDiffValue<T> =
override fun atan(arg: AutoDiffValue<T>): AutoDiffValue<T> =
(this as SimpleAutoDiffField<T, F>).atan(arg)
public override fun sinh(arg: AutoDiffValue<T>): AutoDiffValue<T> =
override fun sinh(arg: AutoDiffValue<T>): AutoDiffValue<T> =
(this as SimpleAutoDiffField<T, F>).sinh(arg)
public override fun cosh(arg: AutoDiffValue<T>): AutoDiffValue<T> =
override fun cosh(arg: AutoDiffValue<T>): AutoDiffValue<T> =
(this as SimpleAutoDiffField<T, F>).cosh(arg)
public override fun tanh(arg: AutoDiffValue<T>): AutoDiffValue<T> =
override fun tanh(arg: AutoDiffValue<T>): AutoDiffValue<T> =
(this as SimpleAutoDiffField<T, F>).tanh(arg)
public override fun asinh(arg: AutoDiffValue<T>): AutoDiffValue<T> =
override fun asinh(arg: AutoDiffValue<T>): AutoDiffValue<T> =
(this as SimpleAutoDiffField<T, F>).asinh(arg)
public override fun acosh(arg: AutoDiffValue<T>): AutoDiffValue<T> =
override fun acosh(arg: AutoDiffValue<T>): AutoDiffValue<T> =
(this as SimpleAutoDiffField<T, F>).acosh(arg)
public override fun atanh(arg: AutoDiffValue<T>): AutoDiffValue<T> =
override fun atanh(arg: AutoDiffValue<T>): AutoDiffValue<T> =
(this as SimpleAutoDiffField<T, F>).atanh(arg)
}

14
kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/SymbolIndexer.kt
View File

@ -10,6 +10,8 @@ import space.kscience.kmath.misc.UnstableKMathAPI
import space.kscience.kmath.nd.Structure2D
import space.kscience.kmath.structures.BufferFactory
import space.kscience.kmath.structures.DoubleBuffer
import kotlin.contracts.InvocationKind
import kotlin.contracts.contract
import kotlin.jvm.JvmInline
/**
@ -75,9 +77,13 @@ public value class SimpleSymbolIndexer(override val symbols: List<Symbol>) : Sym
* Execute the block with symbol indexer based on given symbol order
*/
@UnstableKMathAPI
public inline fun <R> withSymbols(vararg symbols: Symbol, block: SymbolIndexer.() -> R): R =
with(SimpleSymbolIndexer(symbols.toList()), block)
public inline fun <R> withSymbols(vararg symbols: Symbol, block: SymbolIndexer.() -> R): R {
contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
return with(SimpleSymbolIndexer(symbols.toList()), block)
}
@UnstableKMathAPI
public inline fun <R> withSymbols(symbols: Collection<Symbol>, block: SymbolIndexer.() -> R): R =
with(SimpleSymbolIndexer(symbols.toList()), block)
public inline fun <R> withSymbols(symbols: Collection<Symbol>, block: SymbolIndexer.() -> R): R {
contract { callsInPlace(block, InvocationKind.EXACTLY_ONCE) }
return with(SimpleSymbolIndexer(symbols.toList()), block)
}

38
kmath-core/src/commonMain/kotlin/space/kscience/kmath/expressions/specialExpressions.kt Normal file
View File

@ -0,0 +1,38 @@
/*
* 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.expressions
import space.kscience.kmath.operations.ExtendedField
import space.kscience.kmath.structures.Buffer
import space.kscience.kmath.structures.indices
/**
* Generate a chi squared expression from given x-y-sigma data and inline model. Provides automatic
* differentiation.
*/
public fun <T : Any, I : Any, A> AutoDiffProcessor<T, I, A>.chiSquaredExpression(
x: Buffer<T>,
y: Buffer<T>,
yErr: Buffer<T>,
model: A.(I) -> I,
): DifferentiableExpression<T> where A : ExtendedField<I>, A : ExpressionAlgebra<T, I> {
require(x.size == y.size) { "X and y buffers should be of the same size" }
require(y.size == yErr.size) { "Y and yErr buffer should of the same size" }
return differentiate {
var sum = zero
x.indices.forEach {
val xValue = const(x[it])
val yValue = const(y[it])
val yErrValue = const(yErr[it])
val modelValue = model(xValue)
sum += ((yValue - modelValue) / yErrValue).pow(2)
}
sum
}
}

4
kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/BufferedLinearSpace.kt
View File

@ -15,7 +15,7 @@ import space.kscience.kmath.structures.VirtualBuffer
import space.kscience.kmath.structures.indices
public class BufferedLinearSpace<T : Any, A : Ring<T>>(
public class BufferedLinearSpace<T : Any, out A : Ring<T>>(
override val elementAlgebra: A,
private val bufferFactory: BufferFactory<T>,
) : LinearSpace<T, A> {
@ -88,4 +88,4 @@ public class BufferedLinearSpace<T : Any, A : Ring<T>>(
override fun Matrix<T>.times(value: T): Matrix<T> = ndRing(rowNum, colNum).run {
unwrap().map { it * value }.as2D()
}
}
}

4
kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/LinearSolver.kt
View File

@ -6,8 +6,8 @@
package space.kscience.kmath.linear
/**
* A group of methods to solve for *X* in equation *X = A <sup>-1</sup> &middot; B*, where *A* and *B* are matrices or
* vectors.
* A group of methods to solve for *X* in equation *X = A<sup>&minus;1</sup> &middot; B*, where *A* and *B* are
* matrices or vectors.
*
* @param T the type of items.
*/

8
kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/LinearSpace.kt
View File

@ -29,10 +29,10 @@ public typealias MutableMatrix<T> = MutableStructure2D<T>
public typealias Point<T> = Buffer<T>
/**
* Basic operations on matrices and vectors. Operates on [Matrix].
* Basic operations on matrices and vectors.
*
* @param T the type of items in the matrices.
* @param M the type of operated matrices.
* @param A the type of ring over [T].
*/
public interface LinearSpace<T : Any, out A : Ring<T>> {
public val elementAlgebra: A
@ -164,7 +164,7 @@ public interface LinearSpace<T : Any, out A : Ring<T>> {
public operator fun T.times(v: Point<T>): Point<T> = v * this
/**
* Compute a feature of the structure in this scope. Structure features take precedence other context features
* Compute a feature of the structure in this scope. Structure features take precedence other context features.
*
* @param F the type of feature.
* @param structure the structure.
@ -195,7 +195,7 @@ public interface LinearSpace<T : Any, out A : Ring<T>> {
}
/**
* Get a feature of the structure in this scope. Structure features take precedence other context features
* Get a feature of the structure in this scope. Structure features take precedence other context features.
*
* @param T the type of items in the matrices.
* @param F the type of feature.

2
kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/LupDecomposition.kt
View File

@ -115,7 +115,7 @@ public fun <T : Comparable<T>> LinearSpace<T, Field<T>>.lup(
for (i in 0 until col) sum -= luRow[i] * lu[i, col]
luRow[col] = sum
// maintain best permutation choice
// maintain the best permutation choice
if (abs(sum) > largest) {
largest = abs(sum)
max = row

16
kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/MatrixFeatures.kt
View File

@ -31,11 +31,11 @@ public object ZeroFeature : DiagonalFeature
public object UnitFeature : DiagonalFeature
/**
* Matrices with this feature can be inverted: [inverse] = `a`<sup>-1</sup> where `a` is the owning matrix.
* Matrices with this feature can be inverted: *[inverse] = a<sup>&minus;1</sup>* where *a* is the owning matrix.
*
* @param T the type of matrices' items.
*/
public interface InverseMatrixFeature<T : Any> : MatrixFeature {
public interface InverseMatrixFeature<out T : Any> : MatrixFeature {
/**
* The inverse matrix of the matrix that owns this feature.
*/
@ -47,7 +47,7 @@ public interface InverseMatrixFeature<T : Any> : MatrixFeature {
*
* @param T the type of matrices' items.
*/
public interface DeterminantFeature<T : Any> : MatrixFeature {
public interface DeterminantFeature<out T : Any> : MatrixFeature {
/**
* The determinant of the matrix that owns this feature.
*/
@ -80,7 +80,7 @@ public object UFeature : MatrixFeature
*
* @param T the type of matrices' items.
*/
public interface LUDecompositionFeature<T : Any> : MatrixFeature {
public interface LUDecompositionFeature<out T : Any> : MatrixFeature {
/**
* The lower triangular matrix in this decomposition. It may have [LFeature].
*/
@ -98,7 +98,7 @@ public interface LUDecompositionFeature<T : Any> : MatrixFeature {
*
* @param T the type of matrices' items.
*/
public interface LupDecompositionFeature<T : Any> : MatrixFeature {
public interface LupDecompositionFeature<out T : Any> : MatrixFeature {
/**
* The lower triangular matrix in this decomposition. It may have [LFeature].
*/
@ -126,7 +126,7 @@ public object OrthogonalFeature : MatrixFeature
*
* @param T the type of matrices' items.
*/
public interface QRDecompositionFeature<T : Any> : MatrixFeature {
public interface QRDecompositionFeature<out T : Any> : MatrixFeature {
/**
* The orthogonal matrix in this decomposition. It may have [OrthogonalFeature].
*/
@ -144,7 +144,7 @@ public interface QRDecompositionFeature<T : Any> : MatrixFeature {
*
* @param T the type of matrices' items.
*/
public interface CholeskyDecompositionFeature<T : Any> : MatrixFeature {
public interface CholeskyDecompositionFeature<out T : Any> : MatrixFeature {
/**
* The triangular matrix in this decomposition. It may have either [UFeature] or [LFeature].
*/
@ -157,7 +157,7 @@ public interface CholeskyDecompositionFeature<T : Any> : MatrixFeature {
*
* @param T the type of matrices' items.
*/
public interface SingularValueDecompositionFeature<T : Any> : MatrixFeature {
public interface SingularValueDecompositionFeature<out T : Any> : MatrixFeature {
/**
* The matrix in this decomposition. It is unitary, and it consists from left singular vectors.
*/

11
kmath-core/src/commonMain/kotlin/space/kscience/kmath/linear/MatrixWrapper.kt
View File

@ -23,14 +23,15 @@ public class MatrixWrapper<out T : Any> internal constructor(
) : Matrix<T> by origin {
/**
* Get the first feature matching given class. Does not guarantee that matrix has only one feature matching the criteria
* Get the first feature matching given class. Does not guarantee that matrix has only one feature matching the
* criteria.
*/
@UnstableKMathAPI
@Suppress("UNCHECKED_CAST")
public override fun <F : StructureFeature> getFeature(type: KClass<out F>): F? =
override fun <F : StructureFeature> getFeature(type: KClass<out F>): F? =
features.getFeature(type) ?: origin.getFeature(type)
public override fun toString(): String = "MatrixWrapper(matrix=$origin, features=$features)"
override fun toString(): String = "MatrixWrapper(matrix=$origin, features=$features)"
}
/**
@ -64,7 +65,7 @@ public fun <T : Any> Matrix<T>.withFeatures(newFeatures: Iterable<MatrixFeature>
}
/**
* Diagonal matrix of ones. The matrix is virtual no actual matrix is created
* Diagonal matrix of ones. The matrix is virtual no actual matrix is created.
*/
public fun <T : Any> LinearSpace<T, Ring<T>>.one(
rows: Int,
@ -93,4 +94,4 @@ public class TransposedFeature<out T : Any>(public val original: Matrix<T>) : Ma
public fun <T : Any> Matrix<T>.transpose(): Matrix<T> = getFeature<TransposedFeature<T>>()?.original ?: VirtualMatrix(
colNum,
rowNum,
) { i, j -> get(j, i) }.withFeature(TransposedFeature(this))
) { i, j -> get(j, i) }.withFeature(TransposedFeature(this))

6
kmath-core/src/commonMain/kotlin/space/kscience/kmath/misc/annotations.kt
View File

@ -7,8 +7,8 @@ package space.kscience.kmath.misc
/**
* Marks declarations that are still experimental in the KMath APIs, which means that the design of the corresponding
* declarations has open issues which may (or may not) lead to their changes in the future. Roughly speaking, there is
* a chance that those declarations will be deprecated in the near future or the semantics of their behavior may change
* declarations has open issues that may (or may not) lead to their changes in the future. Roughly speaking, there is
* a chance of those declarations will be deprecated in the near future or the semantics of their behavior may change
* in some way that may break some code.
*/
@MustBeDocumented
@ -17,7 +17,7 @@ package space.kscience.kmath.misc
public annotation class UnstableKMathAPI
/**
* Marks API which could cause performance problems. The code, marked by this API is not necessary slow, but could cause
* Marks API that could cause performance problems. The code marked by this API is unnecessary slow but could cause
* slow-down in some cases. Refer to the documentation and benchmark it to be sure.
*/
@MustBeDocumented

2
kmath-core/src/commonMain/kotlin/space/kscience/kmath/misc/cumulative.kt
View File

@ -34,7 +34,7 @@ public inline fun <T, R> Iterable<T>.cumulative(initial: R, crossinline operatio
public inline fun <T, R> Sequence<T>.cumulative(initial: R, crossinline operation: (R, T) -> R): Sequence<R> =
Sequence { this@cumulative.iterator().cumulative(initial, operation) }
public fun <T, R> List<T>.cumulative(initial: R, operation: (R, T) -> R): List<R> =
public inline fun <T, R> List<T>.cumulative(initial: R, crossinline operation: (R, T) -> R): List<R> =
iterator().cumulative(initial, operation).asSequence().toList()
//Cumulative sum

3
kmath-core/src/commonTest/kotlin/space/kscience/kmath/linear/VectorSpaceTest.kt → kmath-core/src/commonMain/kotlin/space/kscience/kmath/misc/numbers.kt
View File

@ -3,3 +3,6 @@
* 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.misc
public expect fun Long.toIntExact(): Int

41
kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/AlgebraND.kt
View File

@ -11,7 +11,7 @@ import space.kscience.kmath.structures.*
import kotlin.reflect.KClass
/**
* An exception is thrown when the expected ans actual shape of NDArray differs.
* An exception is thrown when the expected and actual shape of NDArray differ.
*
* @property expected the expected shape.
* @property actual the actual shape.
@ -24,7 +24,6 @@ public class ShapeMismatchException(public val expected: IntArray, public val ac
*
* @param T the type of ND-structure element.
* @param C the type of the element context.
* @param N the type of the structure.
*/
public interface AlgebraND<T, out C : Algebra<T>> {
/**
@ -64,7 +63,7 @@ public interface AlgebraND<T, out C : Algebra<T>> {
structure.map { value -> this@invoke(value) }
/**
* Get a feature of the structure in this scope. Structure features take precedence other context features
* Get a feature of the structure in this scope. Structure features take precedence other context features.
*
* @param F the type of feature.
* @param structure the structure.
@ -80,7 +79,7 @@ public interface AlgebraND<T, out C : Algebra<T>> {
/**
* Get a feature of the structure in this scope. Structure features take precedence other context features
* Get a feature of the structure in this scope. Structure features take precedence other context features.
*
* @param T the type of items in the matrices.
* @param F the type of feature.
@ -118,8 +117,7 @@ internal fun <T, C : Algebra<T>> AlgebraND<T, C>.checkShape(element: StructureND
* Space of [StructureND].
*
* @param T the type of the element contained in ND structure.
* @param N the type of ND structure.
* @param S the type of space of structure elements.
* @param S the type of group over structure elements.
*/
public interface GroupND<T, out S : Group<T>> : Group<StructureND<T>>, AlgebraND<T, S> {
/**
@ -129,18 +127,9 @@ public interface GroupND<T, out S : Group<T>> : Group<StructureND<T>>, AlgebraND
* @param b the addend.
* @return the sum.
*/
public override fun add(a: StructureND<T>, b: StructureND<T>): StructureND<T> =
override fun add(a: StructureND<T>, b: StructureND<T>): StructureND<T> =
combine(a, b) { aValue, bValue -> add(aValue, bValue) }
// /**
// * Element-wise multiplication by scalar.
// *
// * @param a the multiplicand.
// * @param k the multiplier.
// * @return the product.
// */
// public override fun multiply(a: NDStructure<T>, k: Number): NDStructure<T> = a.map { multiply(it, k) }
// TODO move to extensions after KEEP-176
/**
@ -186,8 +175,7 @@ public interface GroupND<T, out S : Group<T>> : Group<StructureND<T>>, AlgebraND
* Ring of [StructureND].
*
* @param T the type of the element contained in ND structure.
* @param N the type of ND structure.
* @param R the type of ring of structure elements.
* @param R the type of ring over structure elements.
*/
public interface RingND<T, out R : Ring<T>> : Ring<StructureND<T>>, GroupND<T, R> {
/**
@ -197,7 +185,7 @@ public interface RingND<T, out R : Ring<T>> : Ring<StructureND<T>>, GroupND<T, R
* @param b the multiplier.
* @return the product.
*/
public override fun multiply(a: StructureND<T>, b: StructureND<T>): StructureND<T> =
override fun multiply(a: StructureND<T>, b: StructureND<T>): StructureND<T> =
combine(a, b) { aValue, bValue -> multiply(aValue, bValue) }
//TODO move to extensions after KEEP-176
@ -227,9 +215,9 @@ public interface RingND<T, out R : Ring<T>> : Ring<StructureND<T>>, GroupND<T, R
* Field of [StructureND].
*
* @param T the type of the element contained in ND structure.
* @param F the type field of structure elements.
* @param F the type field over structure elements.
*/
public interface FieldND<T, out F : Field<T>> : Field<StructureND<T>>, RingND<T, F>, ScaleOperations<StructureND<T>> {
public interface FieldND<T, out F : Field<T>> : Field<StructureND<T>>, RingND<T, F> {
/**
* Element-wise division.
*
@ -237,7 +225,7 @@ public interface FieldND<T, out F : Field<T>> : Field<StructureND<T>>, RingND<T,
* @param b the divisor.
* @return the quotient.
*/
public override fun divide(a: StructureND<T>, b: StructureND<T>): StructureND<T> =
override fun divide(a: StructureND<T>, b: StructureND<T>): StructureND<T> =
combine(a, b) { aValue, bValue -> divide(aValue, bValue) }
//TODO move to extensions after KEEP-176
@ -259,6 +247,15 @@ public interface FieldND<T, out F : Field<T>> : Field<StructureND<T>>, RingND<T,
*/
public operator fun T.div(arg: StructureND<T>): StructureND<T> = arg.map { divide(it, this@div) }
/**
* Element-wise scaling.
*
* @param a the multiplicand.
* @param value the multiplier.
* @return the product.
*/
override fun scale(a: StructureND<T>, value: Double): StructureND<T> = a.map { scale(it, value) }
// @ThreadLocal
// public companion object {
// private val realNDFieldCache: MutableMap<IntArray, RealNDField> = hashMapOf()

6
kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/BufferAlgebraND.kt
View File

@ -57,7 +57,7 @@ public interface BufferAlgebraND<T, out A : Algebra<T>> : AlgebraND<T, A> {
}
}
public open class BufferedGroupND<T, A : Group<T>>(
public open class BufferedGroupND<T, out A : Group<T>>(
final override val shape: IntArray,
final override val elementContext: A,
final override val bufferFactory: BufferFactory<T>,
@ -67,7 +67,7 @@ public open class BufferedGroupND<T, A : Group<T>>(
override fun StructureND<T>.unaryMinus(): StructureND<T> = produce { -get(it) }
}
public open class BufferedRingND<T, R : Ring<T>>(
public open class BufferedRingND<T, out R : Ring<T>>(
shape: IntArray,
elementContext: R,
bufferFactory: BufferFactory<T>,
@ -75,7 +75,7 @@ public open class BufferedRingND<T, R : Ring<T>>(
override val one: BufferND<T> by lazy { produce { one } }
}
public open class BufferedFieldND<T, R : Field<T>>(
public open class BufferedFieldND<T, out R : Field<T>>(
shape: IntArray,
elementContext: R,
bufferFactory: BufferFactory<T>,

48
kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/DoubleFieldND.kt
View File

@ -22,15 +22,15 @@ public class DoubleFieldND(
ScaleOperations<StructureND<Double>>,
ExtendedField<StructureND<Double>> {
public override val zero: BufferND<Double> by lazy { produce { zero } }
public override val one: BufferND<Double> by lazy { produce { one } }
override val zero: BufferND<Double> by lazy { produce { zero } }
override val one: BufferND<Double> by lazy { produce { one } }
public override fun number(value: Number): BufferND<Double> {
override fun number(value: Number): BufferND<Double> {
val d = value.toDouble() // minimize conversions
return produce { d }
}
public override val StructureND<Double>.buffer: DoubleBuffer
override val StructureND<Double>.buffer: DoubleBuffer
get() = when {
!shape.contentEquals(this@DoubleFieldND.shape) -> throw ShapeMismatchException(
this@DoubleFieldND.shape,
@ -41,7 +41,7 @@ public class DoubleFieldND(
}
@Suppress("OVERRIDE_BY_INLINE")
public override inline fun StructureND<Double>.map(
override inline fun StructureND<Double>.map(
transform: DoubleField.(Double) -> Double,
): BufferND<Double> {
val buffer = DoubleBuffer(strides.linearSize) { offset -> DoubleField.transform(buffer.array[offset]) }
@ -49,7 +49,7 @@ public class DoubleFieldND(
}
@Suppress("OVERRIDE_BY_INLINE")
public override inline fun produce(initializer: DoubleField.(IntArray) -> Double): BufferND<Double> {
override inline fun produce(initializer: DoubleField.(IntArray) -> Double): BufferND<Double> {
val array = DoubleArray(strides.linearSize) { offset ->
val index = strides.index(offset)
DoubleField.initializer(index)
@ -58,7 +58,7 @@ public class DoubleFieldND(
}
@Suppress("OVERRIDE_BY_INLINE")
public override inline fun StructureND<Double>.mapIndexed(
override inline fun StructureND<Double>.mapIndexed(
transform: DoubleField.(index: IntArray, Double) -> Double,
): BufferND<Double> = BufferND(
strides,
@ -70,7 +70,7 @@ public class DoubleFieldND(
})
@Suppress("OVERRIDE_BY_INLINE")
public override inline fun combine(
override inline fun combine(
a: StructureND<Double>,
b: StructureND<Double>,
transform: DoubleField.(Double, Double) -> Double,
@ -81,26 +81,26 @@ public class DoubleFieldND(
return BufferND(strides, buffer)
}
public override fun scale(a: StructureND<Double>, value: Double): StructureND<Double> = a.map { it * value }
override fun scale(a: StructureND<Double>, value: Double): StructureND<Double> = a.map { it * value }
public override fun power(arg: StructureND<Double>, pow: Number): BufferND<Double> = arg.map { power(it, pow) }
override fun power(arg: StructureND<Double>, pow: Number): BufferND<Double> = arg.map { power(it, pow) }
public override fun exp(arg: StructureND<Double>): BufferND<Double> = arg.map { exp(it) }
public override fun ln(arg: StructureND<Double>): BufferND<Double> = arg.map { ln(it) }
override fun exp(arg: StructureND<Double>): BufferND<Double> = arg.map { exp(it) }
override fun ln(arg: StructureND<Double>): BufferND<Double> = arg.map { ln(it) }
public override fun sin(arg: StructureND<Double>): BufferND<Double> = arg.map { sin(it) }
public override fun cos(arg: StructureND<Double>): BufferND<Double> = arg.map { cos(it) }
public override fun tan(arg: StructureND<Double>): BufferND<Double> = arg.map { tan(it) }
public override fun asin(arg: StructureND<Double>): BufferND<Double> = arg.map { asin(it) }
public override fun acos(arg: StructureND<Double>): BufferND<Double> = arg.map { acos(it) }
public override fun atan(arg: StructureND<Double>): BufferND<Double> = arg.map { atan(it) }
override fun sin(arg: StructureND<Double>): BufferND<Double> = arg.map { sin(it) }
override fun cos(arg: StructureND<Double>): BufferND<Double> = arg.map { cos(it) }
override fun tan(arg: StructureND<Double>): BufferND<Double> = arg.map { tan(it) }
override fun asin(arg: StructureND<Double>): BufferND<Double> = arg.map { asin(it) }
override fun acos(arg: StructureND<Double>): BufferND<Double> = arg.map { acos(it) }
override fun atan(arg: StructureND<Double>): BufferND<Double> = arg.map { atan(it) }
public override fun sinh(arg: StructureND<Double>): BufferND<Double> = arg.map { sinh(it) }
public override fun cosh(arg: StructureND<Double>): BufferND<Double> = arg.map { cosh(it) }
public override fun tanh(arg: StructureND<Double>): BufferND<Double> = arg.map { tanh(it) }
public override fun asinh(arg: StructureND<Double>): BufferND<Double> = arg.map { asinh(it) }
public override fun acosh(arg: StructureND<Double>): BufferND<Double> = arg.map { acosh(it) }
public override fun atanh(arg: StructureND<Double>): BufferND<Double> = arg.map { atanh(it) }
override fun sinh(arg: StructureND<Double>): BufferND<Double> = arg.map { sinh(it) }
override fun cosh(arg: StructureND<Double>): BufferND<Double> = arg.map { cosh(it) }
override fun tanh(arg: StructureND<Double>): BufferND<Double> = arg.map { tanh(it) }
override fun asinh(arg: StructureND<Double>): BufferND<Double> = arg.map { asinh(it) }
override fun acosh(arg: StructureND<Double>): BufferND<Double> = arg.map { acosh(it) }
override fun atanh(arg: StructureND<Double>): BufferND<Double> = arg.map { atanh(it) }
}
public fun AlgebraND.Companion.real(vararg shape: Int): DoubleFieldND = DoubleFieldND(shape)

7
kmath-core/src/commonMain/kotlin/space/kscience/kmath/nd/ShortRingND.kt
View File

@ -31,11 +31,10 @@ public class ShortRingND(
/**
* Fast element production using function inlining.
*/
public inline fun BufferedRingND<Short, ShortRing>.produceInline(crossinline initializer: ShortRing.(Int) -> Short): BufferND<Short> {
return BufferND(strides, ShortBuffer(ShortArray(strides.linearSize) { offset -> ShortRing.initializer(offset) }))
}
public inline fun BufferedRingND<Short, ShortRing>.produceInline(crossinline initializer: ShortRing.(Int) -> Short): BufferND<Short> =
BufferND(strides, ShortBuffer(ShortArray(strides.linearSize) { offset -> ShortRing.initializer(offset) }))
public inline fun <R> ShortRing.nd(vararg shape: Int, action: ShortRingND.() -> R): R {
contract { callsInPlace(action, InvocationKind.EXACTLY_ONCE) }
return ShortRingND(shape).run(action)
}
}

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