forked from kscience/kmath
45 lines
2.8 KiB
Markdown
45 lines
2.8 KiB
Markdown
# KMath
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Kotlin MATHematics library is intended as a kotlin based analog of numpy python library. Contrary to `numpy`
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and `scipy` it is modular and has a lightweight core.
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## Features
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* **Algebra**
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* Mathematical operation entities like rings, spaces and fields with (**TODO** add example to wiki)
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* Basic linear algebra operations (sums products, etc) backed by `Space` API.
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* [In progress] advanced linear algebra operations like matrix inversions.
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* **Array-like structures** Full support of numpy-like ndarray including mixed arithmetic operations and function operations
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on arrays and numbers just like it works in python (with benefit of static type checking).
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* **Expressions** Expressions are one of the ultimate goals of kmath. It is planned to be able to write some mathematical
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expression once an then apply it to different types of objects by providing different context. Exception could be used
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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 suite kotlin programming paradigm. There is no fixed priority list for that. Feel free
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to submit a future request if you want something to be done first.
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* **Messaging** A mathematical notation to support multilanguage and multinod 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 common module.
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Implementation is also done in common module wherever it is possible. In some cases features are delegated to
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platform even if they could be done in common module because of platform performance optimization.
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Currently the main focus of development is the JVM platform, contribution of implementations for Kotlin - Native and
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Kotlin - JS is welcome.
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## Performance
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The 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 firstly focus on creating convenient universal API and then work on
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increasing performance for specific cases. We expect the worst KMath performance still be better than natural 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. Work builds could be obtained with
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[](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. |