CLPython
magicl
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| CLPython | magicl | |
|---|---|---|
| 5 | 14 | |
| 364 | 225 | |
| - | 0.4% | |
| 2.9 | 5.4 | |
| 6 months ago | 6 months ago | |
| Common Lisp | Common Lisp | |
| GNU General Public License v3.0 or later | BSD 3-clause "New" or "Revised" License |
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.
CLPython
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Why Static Languages Suffer From Complexity
C++, ~haskell, python, mathematica... capisce? :)
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You loved running JavaScript in your web browser
we need to go deeper https://common-lisp.net/project/clpython/
- Common Lisp implementation in Python
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Anybody using Common Lisp or clojure for data science
cl-python
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Compiler in Lisp
Python
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.
What are some alternatives?
MicroPython - MicroPython - a lean and efficient Python implementation for microcontrollers and constrained systems
lisp-matrix - A matrix package for common lisp building on work by Mark Hoemmen, Evan Monroig, Tamas Papp and Rif.
IronPython - Implementation of Python 3.x for .NET Framework that is built on top of the Dynamic Language Runtime.
py4cl - Call python from Common Lisp
Grumpy - Grumpy is a Python to Go source code transcompiler and runtime.
criterium - Benchmarking library for clojure
PySec - OWASP Python Security Project
Petalisp - Elegant High Performance Computing
pylisp - A Lisp compiler targeting Python
hash-array-mapped-trie - A hash array mapped trie implementation in c.
april - The APL programming language (a subset thereof) compiling to Common Lisp.