# multilinear: Comprehensive and efficient (multi)linear algebra implementation.

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

Comprehensive and efficient (multi)linear algebra implementation, based on generic tensor formalism and concise Ricci-Curbastro index syntax. More information available on GitHub: https://github.com/ArturB/multilinear#readme

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) ChangeLog.md base (>=4.7 && <5), containers (>=0.5), deepseq (>=1.4), mwc-random (>=0.13), primitive (>=0.6), statistics (>=0.14), vector (>=0.12) [details] BSD-3-Clause 2018 Artur M. Brodzki Artur M. Brodzki artur@brodzki.org Machine learning https://github.com/ArturB/multilinear#readme https://github.com/ArturB/multilinear/issues head: git clone https://github.com/ArturB/multilinear by ArturB at Thu Nov 1 21:10:21 UTC 2018 NixOS:0.5.0.0 816 total (155 in the last 30 days) 1.75 (votes: 1) [estimated by rule of succession] λ λ λ Docs available Last success reported on 2018-11-02

#### Maintainer's Corner

For package maintainers and hackage trustees

[back to package description]

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.

## 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