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

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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

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Modules

[Last Documentation]

  • 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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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)
Change log ChangeLog.md
Dependencies base (>=4.7 && <5), containers (>=0.5), deepseq (>=1.4), mwc-random (>=0.13), primitive (>=0.6), statistics (>=0.14), vector (>=0.12) [details]
License BSD-3-Clause
Copyright 2018 Artur M. Brodzki
Author Artur M. Brodzki
Maintainer artur@brodzki.org
Revised Revision 2 made by ArturB at 2018-11-01T20:54:14Z
Category Machine learning
Home page https://github.com/ArturB/multilinear
Bug tracker https://github.com/ArturB/multilinear/issues
Source repo head: git clone https://github.com/ArturB/multilinear
Uploaded by ArturB at 2018-11-01T20:51:03Z
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Reverse Dependencies 1 direct, 1 indirect [details]
Downloads 4480 total (3 in the last 30 days)
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Status Docs not available [build log]
All reported builds failed as of 2018-11-02 [all 3 reports]

Readme for multilinear-0.2.2

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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