statistical-learning
Statistical learning models for the Common Lisp (by sirherrbatka)
magicl
Matrix Algebra proGrams In Common Lisp. (by rigetticomputing)
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| statistical-learning | magicl | |
|---|---|---|
| 1 | 14 | |
| 11 | 225 | |
| - | 0.4% | |
| 7.3 | 5.4 | |
| about 2 months ago | 6 months ago | |
| Common Lisp | Common Lisp | |
| BSD 3-clause "New" or "Revised" License | BSD 3-clause "New" or "Revised" License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
statistical-learning
Posts with mentions or reviews of statistical-learning.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-07-16.
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Anybody using Common Lisp or clojure for data science
Yeah, I use CL for data science, despite lack of suitable tools. I even ended up writing my own: https://github.com/sirherrbatka/clusters https://github.com/sirherrbatka/vellum https://github.com/sirherrbatka/vellum-plot https://github.com/sirherrbatka/statistical-learning
magicl
Posts with mentions or reviews of magicl.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-07-16.
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A tutorial quantum interpreter in 150 lines of Lisp
(Link didn't work for me)
https://github.com/quil-lang/magicl/blob/master/src/high-lev...
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Why Lisp?
use MAGICL. [1] It is optionally and transparently accelerated by BLAS/LAPACK.
[1] https://github.com/quil-lang/magicl/blob/master/doc/high-lev...
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How fast can you multiply matrices using only common lisp?
Maybe have a look at how magicl does this?
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A software engineer's circuitous journey to calculate eigenvalues
This is essentially the first option, which is already supported by MAGICL by loading MAGICL/EXT-LAPACK [1].
[1] https://github.com/quil-lang/magicl#extensions
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Uncle Stats Wants You
I think what the magicl team has done is brilliant - allowing multiple implementations is awesome.
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Good Lisp libraries for math
Second up is magicl, especially useful if performance is a concern. This might not be as extensive as numcl, but it's been battle tested in the industry over the last decade or so. Because this uses generic functions, so long as you are using not-very-small arrays, performance should not be a concern for you. And even if you are, you could write your own functions that use the low-level functions that magicl's backends define. Otherwise performance can be at par with numpy.
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Why is python numpy *so* much faster than lisp in this example?
This Dev How-To describes (I hope in enough detail) how to add these specialized routines to MAGICL.
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CL-AUTOWRAP generated (C)BLAS wrapper in QUICKLISP
I agree... and I do don't want be the person who has not rallied. I just took a look at guicho's issue from 2019. And here, you yourself have admitted that the high level interface is less than ideal and needs more work. However, the very point that magicl is an industry standard could imply that potentially radical backward-incompatible changes can be hard. But, honestly, I want to discuss this, time permitting!
- Fast and Elegant Clojure: Idiomatic Clojure without sacrificing performance
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Anybody using Common Lisp or clojure for data science
Common Lisp is a great language to build new tools for data science, but currently has pretty awful library support existing data science workflows. Common Lisp is sorely lacking in high-quality statistics, plotting, and sparse arrays. There’s been a long work-in-progress library to bring flexible and high-performance linear algebra to Lisp, but it needs more contributors.
What are some alternatives?
When comparing statistical-learning and magicl you can also consider the following projects:
qvm - The high-performance and featureful Quil simulator.
lisp-matrix - A matrix package for common lisp building on work by Mark Hoemmen, Evan Monroig, Tamas Papp and Rif.
numcl-benchmarks - benchmarks against numpy, julia
py4cl - Call python from Common Lisp
neanderthal - Fast Clojure Matrix Library
criterium - Benchmarking library for clojure
vellum-plot
Petalisp - Elegant High Performance Computing
hissp - It's Python with a Lissp.
hash-array-mapped-trie - A hash array mapped trie implementation in c.
vellum - Data Frames for Common Lisp
april - The APL programming language (a subset thereof) compiling to Common Lisp.
statistical-learning vs qvm
magicl vs lisp-matrix
statistical-learning vs numcl-benchmarks
magicl vs py4cl
statistical-learning vs neanderthal
magicl vs criterium
statistical-learning vs vellum-plot
magicl vs Petalisp
statistical-learning vs hissp
magicl vs hash-array-mapped-trie
statistical-learning vs vellum
magicl vs april