numericals
CFFI enabled SIMD powered simple-math numerical operations on arrays for Common Lisp [still experimental] (by digikar99)
mgl
Common Lisp machine learning library. (by melisgl)
numericals | mgl | |
---|---|---|
6 | 15 | |
47 | 573 | |
- | - | |
7.7 | 3.7 | |
about 1 month ago | about 1 year ago | |
Common Lisp | Common Lisp | |
MIT License | MIT 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.
numericals
Posts with mentions or reviews of numericals.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-08-02.
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numericals - Performance of NumPy with the goodness of Common Lisp
How about the semantics? Nevermind, I looked -- utter nonsense, just like numpy.
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Good Lisp libraries for math
Then there is a question - do you actually need these libraries? You can optimize code in Common Lisp (type declarations, usage of appropriate data structures, SIMD instructions etc). See this: https://github.com/digikar99/numericals/tree/master/sbcl-numericals <- SIMD instructions used from SBCL (on x86; these are processor-family specific so Apple M1 will have different ones).
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Image classification in CL? Help with starting point
*I have not; I have a couple of WIP/alpha-stage libraries like dense-arrays and numericals that could be useful; once I find the time, I want to think about if these or its dependencies can be integrated into the existing libraries including antik mentioned by awesome-cl.
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Machine Learning in Lisp
Personally, I've been relying on the stream-based method using py4cl/2, mostly because I did not - and perhaps do not - have the knowledge and time to dig into the CFFI based method. The limitation is that this would get you less than 10000 python interactions per second. That is sufficient if you will be running a long running python task - and I have successfully run trivial ML programs using it, but any intensive array processing gets in the way. For this later task, there are a few emerging libraries like numcl and array-operations without SIMD (yet), and numericals using SIMD. For reasons mentioned on the readme, I recently cooked up dense-arrays. This has interchangeable backends and can also use cl-cuda. But barring that, the developer overhead of actually setting up native-CFFI ecosystem is still too high, and I'm back to py4cl/2 for tasks beyond array processing.
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polymorphic-functions - Possibly AOT dispatch on argument types with support for optional and keyword argument dispatch
I made this while running into code modularity issues with the numericals project I attempted last year; I did discover specialization-store, but found its goals in conflict with what I wanted to achieve; so I ended up investing in this.
mgl
Posts with mentions or reviews of mgl.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-05-13.
- Gabor Melis - Google AI Contest Winner - Conversation and Presentation (2013) (@melisgl, author of MGL)
- MGL: A Common Lisp machine learning library
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Peter Norvig – Paradigms of AI Programming Case Studies in Common Lisp
If you are interested in machine learning, check out Gabor Melis's library: https://github.com/melisgl/mgl. It's not an area I'm super familiar with, so I can't speak to it's feature set, but I believe he used it to win a machine learning competition some years ago.
I don't think anyone's written a transformer or diffusion model with it, could be a fun challenge.
- Mgl: Common Lisp machine learning library
- what library/language combination is good for regression and classification
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New Lisp-Stat Release
although not necessarily bert or resnet the following probably has all the ingredients for what you are looking for. the author of this library is a research scientist at deepmind since 2015
https://github.com/melisgl/mgl#x-28MGL-BP-3A-40MGL-BP-20MGL-...
- Update: Μαθήματα πρόγραμματισμου.
- Why Hy?
What are some alternatives?
When comparing numericals and mgl you can also consider the following projects:
cl-cuda - Cl-cuda is a library to use NVIDIA CUDA in Common Lisp programs.
quilc - The optimizing Quil compiler.
py4cl - Call python from Common Lisp
py4cl2 - Call python from Common Lisp
ghdl - Binary Manager for Github Releases
specialization-store - A different type of generic function for common lisp.
Petalisp - Elegant High Performance Computing
criterium - Benchmarking library for clojure
dense-arrays - Numpy like array object for common lisp
JWM - Cross-platform window management and OS integration library for Java