numhask-array: n-dimensional arrays
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| Versions [RSS] | 0.0.1, 0.0.2, 0.1.0.0, 0.1.1.0, 0.2.0.0, 0.2.0.1, 0.2.1.0, 0.3, 0.3.0.1, 0.4.0.0, 0.5.0.0, 0.5.1, 0.6.0, 0.7.0, 0.8.0, 0.9.0, 0.9.1, 0.9.2, 0.10.0, 0.10.1, 0.10.2, 0.11.0.0, 0.11.0.1 |
|---|---|
| Dependencies | adjunctions (>=4.0 && <5), base (>=4.11 && <5), deepseq (>=1.4.2.0 && <2), distributive (>=0.4 && <0.7), hmatrix (>=0.18), numhask (>=0.6 && <0.7), vector (>=0.10 && <0.13) [details] |
| License | BSD-3-Clause |
| Copyright | Tony Day |
| Author | Tony Day |
| Maintainer | tonyday567@gmail.com |
| Category | project |
| Home page | https://github.com/tonyday567/numhask-array#readme |
| Bug tracker | https://github.com/tonyday567/numhask-array/issues |
| Source repo | head: git clone https://github.com/tonyday567/numhask-array |
| Uploaded | by tonyday567 at 2020-07-15T22:38:41Z |
| Distributions | LTSHaskell:0.11.0.1, NixOS:0.11.0.1, Stackage:0.11.0.1 |
| Reverse Dependencies | 6 direct, 5 indirect [details] |
| Downloads | 6930 total (67 in the last 30 days) |
| Rating | (no votes yet) [estimated by Bayesian average] |
| Your Rating | |
| Status | Docs available [build log] Last success reported on 2020-07-15 [all 1 reports] |
Readme for numhask-array-0.7.0
[back to package description]numhask-array
Arrays are higher-kinded numbers that can be indexed into with an Int list. Higher-kinded numbers are things with a non-primitive type that we wish to use the usual numerical operators on (+,-,*,/,abs).
This is an experimental library that:
- allows shape to be specified at both the type and value level.
- provides operators at value and type level to help manipulate shapes.
- Provides fixed and dynamic arrays with the same API.
Performance experiments are located in numhask-bench
Usefulness of the array language that results from this treatment is yet to be explored.
API of an array language
https://en.wikipedia.org/wiki/APL_(programming_language)
See http://hiperfit.dk/pdf/array14_final.pdf for context and a sketch of an intermediate typed array language effort.
The operators that result from using the Representable type - separation of size tracking at compile level, from computational at runtime - ends up looking like APL.
Matrix multiplication in APL is +.x and in numhask-array is dot sum (*). There is a slight increase in abstraction by explicitly exposing the fold in the algorithm, but the expressions are both very neat and abstracted away from the specialisation of multiplying matrices.
References
https://blog.plover.com/prog/apl-matrix-product.html