magicl
clem
Our great sponsors
| magicl | clem | |
|---|---|---|
| 14 | 1 | |
| 225 | 37 | |
| 0.4% | - | |
| 5.4 | 0.0 | |
| 6 months ago | almost 3 years ago | |
| Common Lisp | Common Lisp | |
| BSD 3-clause "New" or "Revised" License | GNU General Public License v3.0 or later |
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.
magicl
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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.
clem
What are some alternatives?
lisp-matrix - A matrix package for common lisp building on work by Mark Hoemmen, Evan Monroig, Tamas Papp and Rif.
common-lisp-stat - Common Lisp Statistics -- based on LispStat (Tierney) but updated for Common Lisp and incorporating lessons from R (http://www.r-project.org/). See the google group for lisp stat / common lisp statistics for a mailing list.
py4cl - Call python from Common Lisp
criterium - Benchmarking library for clojure
numcl - Numpy clone in Common Lisp
Petalisp - Elegant High Performance Computing
polisher - Infix notation to S-expression (Polish notation) translator for Common Lisp
hash-array-mapped-trie - A hash array mapped trie implementation in c.
physical-quantities - A common lisp library that provides a numeric type with optional unit and/or uncertainty for computations with automatic error propagation.
april - The APL programming language (a subset thereof) compiling to Common Lisp.
array-operations - Common Lisp library that facilitates working with Common Lisp arrays.