Implementation for #139

This commit is contained in:
Alexander Nozik 2020-09-21 15:47:47 +03:00
parent eb8b7196dc
commit bf569e10f9
16 changed files with 271 additions and 30 deletions

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@ -29,7 +29,7 @@ The Kotlin MATHematics library is intended as a Kotlin-based analog to Python's
## Features ## Features
Actual feature list is [here](doc/features.md) Actual feature list is [here](docs/features.md)
* **Algebra** * **Algebra**
* Algebraic structures like rings, spaces and field (**TODO** add example to wiki) * Algebraic structures like rings, spaces and field (**TODO** add example to wiki)
@ -66,6 +66,29 @@ can be used for a wide variety of purposes from high performance calculations to
* **Fitting** Non-linear curve fitting facilities * **Fitting** Non-linear curve fitting facilities
## Modules
### [kmath-core](kmath-core)
- [algebras](kmath-core/src/commonMain/kotlin/kscience/kmath/operations/Algebra.kt) : Algebraic structures: contexts and elements
- [nd](kmath-core/src/commonMain/kotlin/kscience/kmath/structures/NDStructure.kt) : Many-dimensional structures
- [buffers](kmath-core/src/commonMain/kotlin/kscience/kmath/structures/Buffers.kt) : One-dimensional structure
- [expressions](kmath-core/src/commonMain/kotlin/kscience/kmath/expressions) : Functional Expressions
- [domains](kmath-core/src/commonMain/kotlin/kscience/kmath/domains) : Domains
- [autodif](kmath-core/src/commonMain/kotlin/kscience/kmath/misc/AutoDiff.kt) : Automatic differentiation
### [kmath-coroutines](kmath-coroutines)
### [kmath-viktor](kmath-viktor)
### [kmath-prob](kmath-prob)
### [kmath-ast](kmath-ast)
### [kmath-commons](kmath-commons)
### [kmath-memory](kmath-memory)
### [kmath-for-real](kmath-for-real)
### [kmath-functions](kmath-functions)
### [kmath-dimensions](kmath-dimensions)
### [kmath-histograms](kmath-histograms)
### [kmath-geometry](kmath-geometry)
## Multi-platform support ## Multi-platform support
KMath is developed as a multi-platform library, which means that most of interfaces are declared in the [common module](kmath-core/src/commonMain). Implementation is also done in the common module wherever possible. In some cases, features are delegated to platform-specific implementations even if they could be done in the common module for performance reasons. Currently, the JVM is the main focus of development, however Kotlin/Native and Kotlin/JS contributions are also welcome. KMath is developed as a multi-platform library, which means that most of interfaces are declared in the [common module](kmath-core/src/commonMain). Implementation is also done in the common module wherever possible. In some cases, features are delegated to platform-specific implementations even if they could be done in the common module for performance reasons. Currently, the JVM is the main focus of development, however Kotlin/Native and Kotlin/JS contributions are also welcome.
@ -84,8 +107,8 @@ repositories{
} }
dependencies{ dependencies{
api("kscience.kmath:kmath-core:${kmathVersion}") api("kscience.kmath:kmath-core:0.2.0-dev-1")
//api("kscience.kmath:kmath-core-jvm:${kmathVersion}") for jvm-specific version //api("kscience.kmath:kmath-core-jvm:0.2.0-dev-1") for jvm-specific version
} }
``` ```

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@ -18,4 +18,83 @@ allprojects {
version = kmathVersion version = kmathVersion
} }
subprojects { if (name.startsWith("kmath")) apply(plugin = "ru.mipt.npm.publish") } subprojects {
if (name.startsWith("kmath")) apply<ru.mipt.npm.gradle.KSciencePublishPlugin>()
}
/**
* TODO move to base plugin
*/
val generateReadme by tasks.creating {
group = "documentation"
fun List<Map<String, Any?>>.generateFeatureString(pathPrefix: String): String = buildString {
this@generateFeatureString.forEach { feature ->
val id by feature
val description by feature
val ref by feature
appendln(" - [$id]($pathPrefix$ref) : $description")
}
}
doLast {
val reader = groovy.json.JsonSlurper()
val projects = HashMap<String, Map<String, Any?>>()
project.subprojects {
var properties: Map<String, Any?> = mapOf(
"name" to this.name,
"group" to this.group,
"version" to this.version
)
val projectProperties = this.file("docs/kscience-module.json")
@Suppress("UNCHECKED_CAST")
if (projectProperties.exists()) {
val customProperties: Map<String, Any?> =
(reader.parse(projectProperties) as? Map<String, Any?> ?: emptyMap()).withDefault { null }
val features: List<Map<String, Any?>>? by customProperties
val featureString = features?.generateFeatureString("")
properties = customProperties + properties + ("featuresString" to featureString)
}
projects[name] = properties.withDefault { null }
val readmeStub = this.file("docs/README-STUB.md")
if (readmeStub.exists()) {
val readmeFile = this.file("README.md")
readmeFile.writeText(
groovy.text.SimpleTemplateEngine().createTemplate(readmeStub).make(properties).toString()
)
}
}
val rootReadmeStub = project.file("docs/README-STUB.md")
val modulesString = buildString {
projects.filter { it.key.startsWith("kmath") }.forEach { (name, properties) ->
appendln("### [$name]($name)")
val features: List<Map<String, Any?>>? by properties
if (features != null) {
appendln(features!!.generateFeatureString("$name/"))
}
}
}
val rootReadmeProperties: Map<String, Any> = mapOf(
"name" to project.name,
"group" to project.group,
"version" to project.version,
"modulesString" to modulesString
)
if (rootReadmeStub.exists()) {
val readmeFile = project.file("README.md")
readmeFile.writeText(
groovy.text.SimpleTemplateEngine().createTemplate(rootReadmeStub).make(rootReadmeProperties).toString()
)
}
}
}

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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

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docs/README-STUB.md Normal file
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@ -0,0 +1,110 @@
[![JetBrains Research](https://jb.gg/badges/research.svg)](https://confluence.jetbrains.com/display/ALL/JetBrains+on+GitHub)
[![DOI](https://zenodo.org/badge/129486382.svg)](https://zenodo.org/badge/latestdoi/129486382)
![Gradle build](https://github.com/mipt-npm/kmath/workflows/Gradle%20build/badge.svg)
Bintray: [ ![Download](https://api.bintray.com/packages/mipt-npm/kscience/kmath-core/images/download.svg) ](https://bintray.com/mipt-npm/kscience/kmath-core/_latestVersion)
Bintray-dev: [ ![Download](https://api.bintray.com/packages/mipt-npm/dev/kmath-core/images/download.svg) ](https://bintray.com/mipt-npm/dev/kmath-core/_latestVersion)
# KMath
Could be pronounced as `key-math`.
The Kotlin MATHematics library is intended as a Kotlin-based analog to Python's `numpy` library. In contrast to `numpy` and `scipy` it is modular and has a lightweight core.
## Publications
* [A conceptual article about context-oriented design](https://proandroiddev.com/an-introduction-context-oriented-programming-in-kotlin-2e79d316b0a2)
* [Another article about context-oriented design](https://proandroiddev.com/diving-deeper-into-context-oriented-programming-in-kotlin-3ecb4ec38814)
* [ACAT 2019 conference paper](https://aip.scitation.org/doi/abs/10.1063/1.5130103)
# Goal
* Provide a flexible and powerful API to work with mathematics abstractions in Kotlin-multiplatform (JVM and JS for now and Native in future).
* 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.
* Provide 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 `for-real`, which will give better experience for those, who want to work with specific types.
## Features
Actual feature list is [here](docs/features.md)
* **Algebra**
* Algebraic structures like rings, spaces and field (**TODO** add example to wiki)
* Basic linear algebra operations (sums, products, etc.), backed by the `Space` API.
* Complex numbers backed by the `Field` API (meaning that they will be usable in any structure like vectors and N-dimensional arrays).
* Advanced linear algebra operations like matrix inversion and LU decomposition.
* **Array-like structures** Full support of many-dimensional array-like structures
including mixed arithmetic operations and function operations over arrays and numbers (with the added benefit of static type checking).
* **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.
* **Histograms** Fast multi-dimensional histograms.
* **Streaming** Streaming operations on mathematical objects and objects buffers.
* **Type-safe dimensions** Type-safe dimensions for matrix operations.
* **Commons-math wrapper** It is planned to gradually wrap most parts of [Apache commons-math](http://commons.apache.org/proper/commons-math/)
library in Kotlin code and maybe rewrite some parts to better suit the Kotlin programming paradigm, however there is no fixed roadmap for that. Feel free
to submit a feature request if you want something to be done first.
## Planned features
* **Messaging** A mathematical notation to support multi-language and multi-node communication for mathematical tasks.
* **Array statistics**
* **Integration** Univariate and multivariate integration framework.
* **Probability and distributions**
* **Fitting** Non-linear curve fitting facilities
## Modules
$modulesString
## Multi-platform support
KMath is developed as a multi-platform library, which means that most of interfaces are declared in the [common module](kmath-core/src/commonMain). Implementation is also done in the common module wherever possible. In some cases, features are delegated to platform-specific implementations even if they could be done in the common module for performance reasons. Currently, the JVM is the main focus of development, however Kotlin/Native and Kotlin/JS contributions are also welcome.
## Performance
Calculation performance is one of major goals of KMath in the future, but in some cases it is not possible to achieve both performance and flexibility. We expect to focus on creating convenient universal API first and then work on increasing performance for specific cases. We expect the worst KMath benchmarks will perform better than native Python, but worse than optimized native/SciPy (mostly due to boxing operations on primitive numbers). The best performance of optimized parts could be better than SciPy.
### Dependency
Release artifacts are accessible from bintray with following configuration (see documentation for [kotlin-multiplatform](https://kotlinlang.org/docs/reference/multiplatform.html) form more details):
```kotlin
repositories{
maven("https://dl.bintray.com/mipt-npm/kscience")
}
dependencies{
api("kscience.kmath:kmath-core:$version")
//api("kscience.kmath:kmath-core-jvm:$version") for jvm-specific version
}
```
Gradle `6.0+` is required for multiplatform artifacts.
### Development
Development builds are accessible from the reposirtory
```kotlin
repositories{
maven("https://dl.bintray.com/mipt-npm/dev")
}
```
with the same artifact names.
## Contributing
The project requires a lot of additional work. Please feel free to contribute in any way and propose new features.

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docs/features.md Normal file
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@ -0,0 +1,14 @@
# Features
* [Algebra](algebra.md) - [Context-based](contexts.md) operations on different primitives and structures.
* [NDStructures](nd-structure.md)
* [Linear algebra](linear.md) - Matrices, operations and linear equations solving. To be moved to separate module. Currently supports basic
api and multiple library back-ends.
* [Histograms](histograms.md) - Multidimensional histogram calculation and operations.
* [Expressions](expressions.md)
* Commons math integration

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@ -1,16 +1,10 @@
# The Core Module (`kmath-core`) # The Core Module (`kmath-core`)
The core features of KMath: The core features of KMath:
$featuresString
- Algebraic structures: contexts and elements.
- ND structures.
- Buffers.
- Functional Expressions.
- Domains.
- Automatic differentiation.
> #### Artifact: > #### Artifact:
> This module is distributed in the artifact `kscience.kmath:kmath-core:0.1.4-dev-8`. > This module artifact: `$group:kmath-core:$version`.
> >
> **Gradle:** > **Gradle:**
> >
@ -22,7 +16,7 @@ The core features of KMath:
> } > }
> >
> dependencies { > dependencies {
> implementation 'kscience.kmath:kmath-core:0.1.4-dev-8' > implementation '$group:kmath-core:$version'
> } > }
> ``` > ```
> **Gradle Kotlin DSL:** > **Gradle Kotlin DSL:**
@ -35,6 +29,6 @@ The core features of KMath:
> } > }
> >
> dependencies { > dependencies {
> implementation("kscience.kmath:kmath-core:0.1.4-dev-8") > implementation("$group:kmath-core:$version")
> } > }
> ``` > ```

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@ -0,0 +1,36 @@
{
"description": "Core classes, algebra definitions, basic linear algebra",
"maturity": "development",
"features": [
{
"id": "algebras",
"description": "Algebraic structures: contexts and elements",
"ref": "src/commonMain/kotlin/kscience/kmath/operations/Algebra.kt"
},
{
"id": "nd",
"description": "Many-dimensional structures",
"ref": "src/commonMain/kotlin/kscience/kmath/structures/NDStructure.kt"
},
{
"id": "buffers",
"description": "One-dimensional structure",
"ref": "src/commonMain/kotlin/kscience/kmath/structures/Buffers.kt"
},
{
"id": "expressions",
"description": "Functional Expressions",
"ref": "src/commonMain/kotlin/kscience/kmath/expressions"
},
{
"id": "domains",
"description": "Domains",
"ref": "src/commonMain/kotlin/kscience/kmath/domains"
},
{
"id": "autodif",
"description": "Automatic differentiation",
"ref": "src/commonMain/kotlin/kscience/kmath/misc/AutoDiff.kt"
}
]
}

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@ -32,9 +32,8 @@ include(
":kmath-histograms", ":kmath-histograms",
":kmath-commons", ":kmath-commons",
":kmath-viktor", ":kmath-viktor",
":kmath-koma",
":kmath-prob", ":kmath-prob",
":kmath-io", // ":kmath-io",
":kmath-dimensions", ":kmath-dimensions",
":kmath-for-real", ":kmath-for-real",
":kmath-geometry", ":kmath-geometry",