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# KMath
Kotlin MATHematics library is intended as a kotlin based analog of numpy python library. Contrary to `numpy`
and `scipy` it is modular and has a lightweight core.
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.
## Features
* **Algebra**
* Mathematical operation entities like rings, spaces and fields with (**TODO** add example to wiki)
* Basic linear algebra operations (summs products, etc) backed by `Space` API.
* Complex numbers backed by `Field` API (meaning that they will be useable in any structures like vectors and NDArrays).
* [In progress] advanced linear algebra operations like matrix inversions.
* **Array-like structures** Full support of numpy-like ndarray including mixed arithmetic operations and function operations
on arrays and numbers just like it works in python (with benefit of static type checking).
* 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).
* [In progress] advanced linear algebra operations like matrix inversion and LU decomposition.
* **Array-like structures** Full support of [numpy-like ndarrays](https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.ndarray.html) including mixed arithmetic operations and function operations over arrays and numbers just like in Python (with the added benefit of static type checking).
* **Expressions** Expressions are one of the ultimate goals of kmath. It is planned to be able to write some mathematical
expression once an then apply it to different types of objects by providing different context. Exception could be used
for a wide variety of purposes from high performance calculations to code generation.
* **Expressions** Expressions are one of the ultimate goals of KMath. By writing a single mathematical expression
once, users will be able to apply different types of objects to the expression by providing a context. Exceptions
can be used for a wide variety of purposes from high performance calculations to code generation.
## Planned features
* **Common mathematics** 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 suite kotlin programming paradigm. There is no fixed priority list for that. Feel free
to submit a future request if you want something to be done first.
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.
* **Messaging** A mathematical notation to support multi-language and multi-node communication for mathematical tasks.
## Multi-platform support
KMath is developed as a multi-platform library, which means that most of interfaces are declared in common module.
Implementation is also done in common module wherever it is possible. In some cases features are delegated to
platform even if they could be done in common module because of platform performance optimization.
Currently the main focus of development is the JVM platform, contribution of implementations for Kotlin - Native and
Kotlin - JS is 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.
## Performance
The 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 firstly focus on creating convenient universal API and then work on
increasing performance for specific cases. We expect the worst KMath performance still be better than natural 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.
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.
## Releases
The project is currently in pre-release stage. Nightly builds could be used by adding additional repository to (groovy) gradle config:
The project is currently in pre-release stage. Nightly builds can be used by adding an additional repository to the Gradle config like so:
```groovy
repositories {
maven { url = "http://npm.mipt.ru:8081/artifactory/gradle-dev" }
mavenCentral()
}
```
or for kotlin gradle dsl:
or for the Gradle Kotlin DSL:
```kotlin
repositories {
@ -56,16 +57,20 @@ repositories {
}
```
Then use regular dependency like
Then use a regular dependency like so:
```groovy
compile(group: 'scientifik', name: 'kmath-core', version: '0.0.1-SNAPSHOT')
```
or in kotlin
or in the Gradle Kotlin DSL:
```kotlin
compile(group = "scientifik", name = "kmath-core", version = "0.0.1-SNAPSHOT")
```
Work builds could be obtained with [![](https://jitpack.io/v/altavir/kmath.svg)](https://jitpack.io/#altavir/kmath).
Working builds can be obtained here: [![](https://jitpack.io/v/altavir/kmath.svg)](https://jitpack.io/#altavir/kmath).
## Contributing
The project requires a lot of additional work. Please fill free to contribute in any way and propose new features.