multilinear: Efficient (multi)linear algebra implementation

[ bsd3, library, machine-learning ] [ Propose Tags ]

Please see the README on GitHub at https://github.com/ArturB/multilinear#readme


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Versions [faq] 0.2.0, 0.2.1, 0.2.2, 0.2.2.1, 0.2.3.0, 0.3.0.0, 0.3.1.0, 0.3.2.0, 0.4.0.0, 0.5.0.0 (info)
Dependencies base (>=4.7 && <5), containers, deepseq, mwc-random, primitive, statistics, vector [details]
License BSD-3-Clause
Copyright 2018 Artur M. Brodzki
Author Artur M. Brodzki
Maintainer artur@brodzki.org
Category Machine learning
Home page https://github.com/ArturB/multilinear#readme
Bug tracker https://github.com/ArturB/multilinear/issues
Source repo head: git clone https://github.com/ArturB/multilinear
Uploaded by ArturB at Wed Oct 31 23:42:42 UTC 2018
Distributions NixOS:0.5.0.0
Downloads 814 total (163 in the last 30 days)
Rating 1.75 (votes: 1) [estimated by rule of succession]
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Status Hackage Matrix CI
Docs pending
Build status unknown [no reports yet]

Modules

  • Multilinear
    • Multilinear.Class
    • Multilinear.Form
    • Multilinear.Generic
    • Multilinear.Index
      • Multilinear.Index.Finite
      • Multilinear.Index.Infinite
    • Multilinear.Matrix
    • Multilinear.NForm
    • Multilinear.NVector
    • Multilinear.Tensor
    • Multilinear.Vector

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Readme for multilinear-0.2.0

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README

Build Status

Multilinear is general - purpose linear algebra and multi-dimensional array library for Haskell. It provides generic and efficient implementation of linear algebra operations on vectors, linear functionals, matrices and its higher - rank analoges: tensors. It can also be used as simply a miltidimensional arrays for everyone.

AS FOR NOW, THE LIBRARY IS IN PRODUCTION PHASE - DO NOT USE IT FOR PRODUCTION!!

Scripting

Multilinear is optimized to being used from GHCi. It uses easy and concise notation of Einstein summation convention to calculate complex tasks. Using this, you are able to write for example a deep learnin neural network from scratch in just a few lines of interpreter code. If you want to know more about Einstein convention, see the Wikipedia: https://en.wikipedia.org/wiki/Einstein_notation

Machine learning

Multi-dimensional algebra is especially useful to quickly write machine learning algorithms (eg. neural networks) from scratch. When library will be stable, some examples will be available.

Installation

Installation using Stack. Invoke this command in library folder:

stack build

Contribution guidelines

If you want to contribute to this library, contact with me.

Who do I talk to?

All copyrights to Artur M. Brodzki. Contact mail: artur@brodzki.org