forked from kscience/kmath
255 lines
9.4 KiB
Markdown
255 lines
9.4 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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Bintray: [  ](https://bintray.com/mipt-npm/kscience/kmath-core/_latestVersion)
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Bintray-dev: [  ](https://bintray.com/mipt-npm/dev/kmath-core/_latestVersion)
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# KMath
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Could be pronounced as `key-math`. The Kotlin MATHematics library was initially intended as a Kotlin-based analog to
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Python's NumPy library. Later we found that kotlin is much more flexible language and allows superior architecture
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designs. In contrast to `numpy` and `scipy` it is modular and has a lightweight core. The `numpy`-like experience could
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be achieved with [kmath-for-real](/kmath-for-real) extension module.
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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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* 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 terms of 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 `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
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Current feature list is [here](/docs/features.md)
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* **Algebra**
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* Algebraic structures like rings, spaces and fields (**TODO** add example to wiki)
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* Basic linear algebra operations (sums, products, etc.), backed by the `Space` API.
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* Complex numbers backed by the `Field` API (meaning they will be usable in any structure like vectors and
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N-dimensional arrays).
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* Advanced linear algebra operations like matrix inversion and LU decomposition.
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* **Array-like structures** Full support of many-dimensional array-like structures
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including mixed arithmetic operations and function operations over arrays and numbers (with the added benefit of static type checking).
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* **Expressions** By writing a single mathematical expression once, users will be able to apply different types of
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objects to the expression by providing a context. Expressions can be used for a wide variety of purposes from high
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performance calculations to code generation.
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* **Histograms** Fast multi-dimensional histograms.
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* **Streaming** Streaming operations on mathematical objects and objects buffers.
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* **Type-safe dimensions** Type-safe dimensions for matrix operations.
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* **Commons-math wrapper** It is planned to gradually wrap most parts of
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[Apache commons-math](http://commons.apache.org/proper/commons-math/) library in Kotlin code and maybe rewrite some
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parts to better suit the Kotlin programming paradigm, however there is no established roadmap for that. Feel free to
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submit a feature request if you want something to be implemented first.
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## Planned features
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* **Messaging** A mathematical notation to support multi-language and multi-node communication for mathematical tasks.
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* **Array statistics**
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* **Integration** Univariate and multivariate integration framework.
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* **Probability and distributions**
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* **Fitting** Non-linear curve fitting facilities
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## Modules
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<hr/>
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* ### [examples](examples)
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>
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>
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> **Maturity**: EXPERIMENTAL
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<hr/>
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* ### [kmath-ast](kmath-ast)
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>
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>
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> **Maturity**: EXPERIMENTAL
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<hr/>
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* ### [kmath-commons](kmath-commons)
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>
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>
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> **Maturity**: EXPERIMENTAL
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<hr/>
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* ### [kmath-core](kmath-core)
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> Core classes, algebra definitions, basic linear algebra
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>
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> **Maturity**: DEVELOPMENT
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>
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> **Features:**
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> - [algebras](kmath-core/src/commonMain/kotlin/kscience/kmath/operations/Algebra.kt) : Algebraic structures: contexts and elements
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> - [nd](kmath-core/src/commonMain/kotlin/kscience/kmath/structures/NDStructure.kt) : Many-dimensional structures
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> - [buffers](kmath-core/src/commonMain/kotlin/kscience/kmath/structures/Buffers.kt) : One-dimensional structure
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> - [expressions](kmath-core/src/commonMain/kotlin/kscience/kmath/expressions) : Functional Expressions
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> - [domains](kmath-core/src/commonMain/kotlin/kscience/kmath/domains) : Domains
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> - [autodif](kmath-core/src/commonMain/kotlin/kscience/kmath/expressions/SimpleAutoDiff.kt) : Automatic differentiation
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<hr/>
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* ### [kmath-coroutines](kmath-coroutines)
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>
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>
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> **Maturity**: EXPERIMENTAL
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<hr/>
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* ### [kmath-dimensions](kmath-dimensions)
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>
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>
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> **Maturity**: EXPERIMENTAL
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<hr/>
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* ### [kmath-ejml](kmath-ejml)
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>
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>
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> **Maturity**: EXPERIMENTAL
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<hr/>
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* ### [kmath-for-real](kmath-for-real)
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> Extension module that should be used to achieve numpy-like behavior.
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All operations are specialized to work with `Double` numbers without declaring algebraic contexts.
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One can still use generic algebras though.
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>
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> **Maturity**: EXPERIMENTAL
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>
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> **Features:**
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> - [RealVector](kmath-for-real/src/commonMain/kotlin/kscience/kmath/real/RealVector.kt) : Numpy-like operations for Buffers/Points
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> - [RealMatrix](kmath-for-real/src/commonMain/kotlin/kscience/kmath/real/RealMatrix.kt) : Numpy-like operations for 2d real structures
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> - [grids](kmath-for-real/src/commonMain/kotlin/kscience/kmath/structures/grids.kt) : Uniform grid generators
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<hr/>
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* ### [kmath-functions](kmath-functions)
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>
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>
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> **Maturity**: EXPERIMENTAL
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<hr/>
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* ### [kmath-geometry](kmath-geometry)
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>
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>
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> **Maturity**: EXPERIMENTAL
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<hr/>
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* ### [kmath-histograms](kmath-histograms)
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>
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>
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> **Maturity**: EXPERIMENTAL
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<hr/>
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* ### [kmath-kotlingrad](kmath-kotlingrad)
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>
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>
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> **Maturity**: EXPERIMENTAL
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<hr/>
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* ### [kmath-memory](kmath-memory)
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>
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>
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> **Maturity**: EXPERIMENTAL
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<hr/>
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* ### [kmath-nd4j](kmath-nd4j)
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> ND4J NDStructure implementation and according NDAlgebra classes
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>
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> **Maturity**: EXPERIMENTAL
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>
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> **Features:**
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> - [nd4jarraystructure](kmath-nd4j/src/commonMain/kotlin/kscience/kmath/operations/Algebra.kt) : NDStructure wrapper for INDArray
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> - [nd4jarrayrings](kmath-nd4j/src/commonMain/kotlin/kscience/kmath/structures/NDStructure.kt) : Rings over Nd4jArrayStructure of Int and Long
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> - [nd4jarrayfields](kmath-nd4j/src/commonMain/kotlin/kscience/kmath/structures/Buffers.kt) : Fields over Nd4jArrayStructure of Float and Double
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<hr/>
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* ### [kmath-stat](kmath-stat)
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>
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>
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> **Maturity**: EXPERIMENTAL
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<hr/>
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* ### [kmath-viktor](kmath-viktor)
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>
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>
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> **Maturity**: EXPERIMENTAL
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<hr/>
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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
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both 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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### Repositories
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Release artifacts are accessible from bintray with following configuration (see documentation of
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[Kotlin Multiplatform](https://kotlinlang.org/docs/reference/multiplatform.html) for more details):
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```kotlin
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repositories {
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maven("https://dl.bintray.com/mipt-npm/kscience")
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}
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dependencies {
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api("kscience.kmath:kmath-core:0.2.0-dev-4")
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// api("kscience.kmath:kmath-core-jvm:0.2.0-dev-4") 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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#### Development
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Development builds are uploaded to the separate repository:
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```kotlin
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repositories {
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maven("https://dl.bintray.com/mipt-npm/dev")
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}
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```
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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,
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especially in issues 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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