forked from kscience/kmath
77 lines
3.6 KiB
Markdown
77 lines
3.6 KiB
Markdown
# KMath
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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.
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## Features
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* **Algebra**
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* Algebraic structures like rings, spaces and field (**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 that they will be usable in any structure like vectors and N-dimensional arrays).
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* [In progress] advanced linear algebra operations like matrix inversion and LU decomposition.
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* **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).
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* **Expressions** Expressions are one of the ultimate goals of KMath. By writing a single mathematical expression
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once, users will be able to apply different types of objects to the expression by providing a context. Exceptions
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can be used for a wide variety of purposes from high performance calculations to code generation.
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## Planned features
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* **Common mathematics** It is planned to gradually wrap most parts of [Apache commons-math](http://commons.apache.org/proper/commons-math/)
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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
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to submit a feature request if you want something to be done first.
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* **Messaging** A mathematical notation to support multi-language and multi-node communication for mathematical tasks.
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## Multi-platform support
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KMath is developed as a multi-platform library, which means that most of interfaces are declared in the [common module](kmath-core/src/commonMain).
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Implementation is also done in the common module wherever possible. In some cases, features are delegated to
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platform-specific implementations even if they could be done in the common module for performance reasons.
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Currently, the JVM is the main focus of development, however Kotlin/Native and Kotlin/JS contributions 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 not possible to achieve
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both performance and flexibility. We expect to focus on creating convenient universal API first and then work on
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increasing performance for specific cases. We expect the worst KMath benchmarks will perform better than native Python,
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but worse than optimized native/SciPy (mostly due to boxing operations on primitive numbers). The best performance
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of optimized parts should be better than SciPy.
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## Releases
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The project is currently in pre-release stage. Nightly builds can be used by adding an additional repository to the Gradle config like so:
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```groovy
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repositories {
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maven { url = "http://npm.mipt.ru:8081/artifactory/gradle-dev" }
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mavenCentral()
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}
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```
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or for the Gradle Kotlin DSL:
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```kotlin
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repositories {
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maven("http://npm.mipt.ru:8081/artifactory/gradle-dev")
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mavenCentral()
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}
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```
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Then use a regular dependency like so:
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```groovy
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api "scientifik:kmath-core-jvm:0.1.0-dev"
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```
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or in the Gradle Kotlin DSL:
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```kotlin
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api("scientifik:kmath-core-jvm:0.1.0-dev")
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```
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Working builds can be obtained here: [](https://jitpack.io/#altavir/kmath).
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## Contributing
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The project requires a lot of additional work. Please fill free to contribute in any way and propose new features.
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