Bintray:

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.

Actual feature list is here

• 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.

• 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 fixed roadmap for that. Feel free to submit a feature request if you want something to be done first.

• Koma wrapper Koma is a well established numerics library in Kotlin, specifically linear algebra. The plan is to have wrappers for koma implementations for compatibility with kmath API.

• 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

KMath is developed as a multi-platform library, which means that most of interfaces are declared in the common module. 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.

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 should be better than SciPy.

Working builds can be obtained here: .

The project is currently in pre-release stage. Nightly builds can be used by adding an additional repository to the Gradle config like so:

repositories {
    maven { url = "https://npm.mipt.ru:8081/artifactory/gradle-dev" }
    mavenCentral()
} 

or for the Gradle Kotlin DSL:

repositories {
    maven("https://npm.mipt.ru:8081/artifactory/gradle-dev")
    mavenCentral()
} 

Then use a regular dependency like so:

api "scientifik:kmath-core-jvm:0.1.0-dev"

or in the Gradle Kotlin DSL:

api("scientifik:kmath-core-jvm:0.1.0-dev")

Release artifacts are accessible from bintray with following configuration:

repositories{
    maven("https://dl.bintray.com/mipt-npm/scientifik")
}

dependencies{
    api("scientifik:kmath-core-jvm:0.1.0")
}

The project requires a lot of additional work. Please feel free to contribute in any way and propose new features.