multilinear: Efficient (multi)linear algebra implementation
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
Downloads
- multilinear-0.2.0.tar.gz [browse] (Cabal source package)
- Package description (as included in the package)
Maintainer's Corner
For package maintainers and hackage trustees
Candidates
| Versions [RSS] | 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 2018-10-31T23:42:42Z |
| Distributions | |
| Reverse Dependencies | 1 direct, 1 indirect [details] |
| Downloads | 4269 total (24 in the last 30 days) |
| Rating | (no votes yet) [estimated by Bayesian average] |
| Your Rating | |
| Status | Docs not available [build log] All reported builds failed as of 2021-05-30 [all 2 reports] |
Readme for multilinear-0.2.0
[back to package description]README
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