forked from kscience/kmath
106 lines
5.9 KiB
Markdown
106 lines
5.9 KiB
Markdown
[](https://confluence.jetbrains.com/display/ALL/JetBrains+on+GitHub)
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[](https://zenodo.org/badge/latestdoi/129486382)
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[](https://search.maven.org/search?q=g:%22space.kscience%22)
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[](https://maven.pkg.jetbrains.space/mipt-npm/p/sci/maven/space/kscience/)
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# KMath
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Could be pronounced as `key-math`. The **K**otlin **Math**ematics library was initially intended as a Kotlin-based
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analog to Python's NumPy library. Later we found that kotlin is much more flexible language and allows superior
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architecture designs. In contrast to `numpy` and `scipy` it is modular and has a lightweight core. The `numpy`-like
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experience could be achieved with [kmath-for-real](/kmath-for-real) extension module.
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[Documentation site (**WIP**)](https://mipt-npm.github.io/kmath/)
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## Publications and talks
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* [A conceptual article about context-oriented design](https://proandroiddev.com/an-introduction-context-oriented-programming-in-kotlin-2e79d316b0a2)
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* [Another article about context-oriented design](https://proandroiddev.com/diving-deeper-into-context-oriented-programming-in-kotlin-3ecb4ec38814)
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* [ACAT 2019 conference paper](https://aip.scitation.org/doi/abs/10.1063/1.5130103)
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# Goal
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* Provide a flexible and powerful API to work with mathematics abstractions in Kotlin-multiplatform (JVM, JS and Native)
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.
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* Provide basic multiplatform implementations for those abstractions (without significant performance optimization).
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* Provide bindings and wrappers with those abstractions for popular optimized platform libraries.
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## Non-goals
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* Be like NumPy. It was the idea at the beginning, but we decided that we can do better in API.
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* Provide the best performance out of the box. We have specialized libraries for that. Need only API wrappers for them.
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* Cover all cases as immediately and in one bundle. We will modularize everything and add new features gradually.
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* Provide specialized behavior in the core. API is made generic on purpose, so one needs to specialize for types, like
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for `Double` in the core. For that we will have specialization modules like `kmath-for-real`, which will give better
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experience for those, who want to work with specific types.
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## Features and stability
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KMath is a modular library. Different modules provide different features with different API stability guarantees. All
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core modules are released with the same version, but with different API change policy. The features are described in
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module definitions below. The module stability could have the following levels:
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* **PROTOTYPE**. On this level there are no compatibility guarantees. All methods and classes form those modules could
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break any moment. You can still use it, but be sure to fix the specific version.
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* **EXPERIMENTAL**. The general API is decided, but some changes could be made. Volatile API is marked
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with `@UnstableKmathAPI` or other stability warning annotations.
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* **DEVELOPMENT**. API breaking generally follows semantic versioning ideology. There could be changes in minor
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versions, but not in patch versions. API is protected
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with [binary-compatibility-validator](https://github.com/Kotlin/binary-compatibility-validator) tool.
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* **STABLE**. The API stabilized. Breaking changes are allowed only in major releases.
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## Modules
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${modules}
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## Multi-platform support
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KMath is developed as a multi-platform library, which means that most of the interfaces are declared in the
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[common source sets](/kmath-core/src/commonMain) and implemented there wherever it is possible. In some cases, features
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are delegated to platform-specific implementations even if they could be provided in the common module for performance
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reasons. Currently, the Kotlin/JVM is the primary platform, however Kotlin/Native and Kotlin/JS contributions and
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feedback are also welcome.
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## Performance
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Calculation performance is one of major goals of KMath in the future, but in some cases it is impossible to achieve both
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performance and flexibility.
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We expect to focus on creating convenient universal API first and then work on increasing performance for specific
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cases. We expect the worst KMath benchmarks will perform better than native Python, but worse than optimized
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native/SciPy (mostly due to boxing operations on primitive numbers). The best performance of optimized parts could be
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better than SciPy.
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## Requirements
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KMath currently relies on JDK 11 for compilation and execution of Kotlin-JVM part. We recommend to use GraalVM-CE 11 for
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execution to get better performance.
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### Repositories
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Release and development artifacts are accessible from mipt-npm [Space](https://www.jetbrains.com/space/)
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repository `https://maven.pkg.jetbrains.space/mipt-npm/p/sci/maven` (see documentation of
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[Kotlin Multiplatform](https://kotlinlang.org/docs/reference/multiplatform.html) for more details). The repository could
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be reached through [repo.kotlin.link](https://repo.kotlin.link) proxy:
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```kotlin
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repositories {
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maven("https://repo.kotlin.link")
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}
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dependencies {
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api("${group}:kmath-core:$version")
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// api("${group}:kmath-core-jvm:$version") for jvm-specific version
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}
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```
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Gradle `6.0+` is required for multiplatform artifacts.
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## Contributing
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The project requires a lot of additional work. The most important thing we need is a feedback about what features are
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required the most. Feel free to create feature requests. We are also welcome to code contributions, especially in issues
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marked with
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[waiting for a hero](https://github.com/mipt-npm/kmath/labels/waiting%20for%20a%20hero) label.
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