9.4 KiB
KMath
Could be pronounced as key-math
. The Kotlin MATHematics 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 extension module.
Publications and talks
- A conceptual article about context-oriented design
- Another article about context-oriented design
- ACAT 2019 conference paper
Goal
- 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.
- 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 likefor-real
, which will give better experience for those, who want to work with specific types.
Features
Current feature list is here
-
Algebra
- Algebraic structures like rings, spaces and fields (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 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 library in Kotlin code and maybe rewrite some parts to better suit the Kotlin programming paradigm, however there is no established roadmap for that. Feel free to submit a feature request if you want something to be implemented 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
Maturity: EXPERIMENTAL
Maturity: EXPERIMENTAL
Maturity: EXPERIMENTAL
Core classes, algebra definitions, basic linear algebra
Maturity: DEVELOPMENT
Features:
Maturity: EXPERIMENTAL
Maturity: EXPERIMENTAL
Maturity: EXPERIMENTAL
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.Maturity: EXPERIMENTAL
Features:
- RealVector : Numpy-like operations for Buffers/Points
- RealMatrix : Numpy-like operations for 2d real structures
- grids : Uniform grid generators
Maturity: EXPERIMENTAL
Maturity: EXPERIMENTAL
Maturity: EXPERIMENTAL
Maturity: EXPERIMENTAL
Maturity: EXPERIMENTAL
ND4J NDStructure implementation and according NDAlgebra classes
Maturity: EXPERIMENTAL
Features:
- nd4jarraystructure : NDStructure wrapper for INDArray
- nd4jarrayrings : Rings over Nd4jArrayStructure of Int and Long
- nd4jarrayfields : Fields over Nd4jArrayStructure of Float and Double
Maturity: EXPERIMENTAL
Maturity: EXPERIMENTAL
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 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.
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.
Repositories
Release artifacts are accessible from bintray with following configuration (see documentation of Kotlin Multiplatform for more details):
repositories {
maven("https://dl.bintray.com/mipt-npm/kscience")
}
dependencies {
api("kscience.kmath:kmath-core:0.2.0-dev-4")
// api("kscience.kmath:kmath-core-jvm:0.2.0-dev-4") for jvm-specific version
}
Gradle 6.0+
is required for multiplatform artifacts.
Development
Development builds are uploaded to the separate repository:
repositories {
maven("https://dl.bintray.com/mipt-npm/dev")
}
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 waiting for a hero label.