polymorphic-functions
A function type to dispatch on types instead of classes with partial support for dispatching on optional and keyword argument types. (by digikar99)
dense-arrays
Numpy like array object for common lisp (by digikar99)
Our great sponsors
polymorphic-functions | dense-arrays | |
---|---|---|
6 | 7 | |
50 | 23 | |
- | - | |
5.6 | 6.3 | |
28 days ago | 30 days ago | |
Common Lisp | Common Lisp | |
- | - |
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.
polymorphic-functions
Posts with mentions or reviews of polymorphic-functions.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-02-24.
-
Adding new types and operators to Lisp
If performance is a concern, then you would want to stick to CLHS provided simple-array and create appropriate types using deftype, and then dispatch on the types either by yourself, or by using something like polymorphic-functions and polymorph.maths.
-
defgeneric and &rest
If you want to dispatch on vectors, you can try out polymorphic-functions which was made for the express purpose of dispatching on specialized arrays aka types rather than classes.
-
numericals - Performance of NumPy with the goodness of Common Lisp
Since the past year or two, I have been working on numericals that aims to provide the speed of NumPy with the goodness of Common Lisp. In particular, this includes the use of dynamic variables, restarts, and compiler-notes wherever appropriate. It uses CLTL2 API (and may be slightly more) under the hood to provide AOT dispatch, but nothing stops you from combining it with JAOT dispatch provided by numcl/specialized-function. This also spawned a number of projects most notably polymorphic-functions to dispatch on types instead of classes and extensible-compound-types that allows one to define user defined compound types (beyond just the type-aliases enabled by deftype. Fortunately enough, interoperation between magicl, numcl and numericals/dense-numericals actually looks plausible!
-
Common Lisp polymorphic stories.
Before reading this, please go and check out https://github.com/digikar99/polymorphic-functions which this project is fully based on. It's great.
-
polymorphic-functions - Possibly AOT dispatch on argument types with support for optional and keyword argument dispatch
What I am calling parametric polymorphism is this test:
dense-arrays
Posts with mentions or reviews of dense-arrays.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-09-20.
- dense-arrays: Numpy like array object for common lisp
-
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.
-
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.
-
polymorphic-functions - Possibly AOT dispatch on argument types with support for optional and keyword argument dispatch
Currently I have put successfully this to use at dense-numericals - which I created over dense-arrays after finding CL arrays to be not that suitable, as compared to numpy or julia. Now, dense-numericals relies on passing the array pointer to C functions. However, IIUC, this runs into issues for what if the GC moves the arrays while the computation is still not done; is this worry valid? I think I ran into this while running multithreaded tests on CCL, ending up in segfaults.
-
Confused about array runtime type checking in SBCL
Shameless unstable plug: I think it should be possible to provide type checking with a different backend that does not upgrade the types at https://github.com/digikar99/dense-arrays - the backend things are themselves unstable though.
-
Past, Present, and Future of Lisp
In semi-production, ideally the problems are best represented using state diagrams, but I don't see a way to comfortably represent graphs in textual formats. The best I see is list of lists, which doesn't feel significantly better than the spaghetti code it currently is (for instance this and this - but these are just about one function each in a larger system, so not totally worth a DSL, unless there existed a defacto state-diagram DSL which everyone could be expected to know.
What are some alternatives?
When comparing polymorphic-functions and dense-arrays you can also consider the following projects:
lisp-interface-library - LIL: abstract interfaces and supporting concrete data-structures in Common Lisp
array-operations - Common Lisp library that facilitates working with Common Lisp arrays.
fast-generic-functions - Seal your generic functions for an extra boost in performance.
py4cl - Call python from Common Lisp
cl-parametric-types - (BETA) C++-style templates for Common Lisp
numericals - CFFI enabled SIMD powered simple-math numerical operations on arrays for Common Lisp [still experimental]
ctype - CL type system implementation
py4cl2 - Call python from Common Lisp
cffi - The Common Foreign Function Interface
specialization-store - A different type of generic function for common lisp.
polymorphic-functions vs lisp-interface-library
dense-arrays vs array-operations
polymorphic-functions vs fast-generic-functions
dense-arrays vs py4cl
polymorphic-functions vs cl-parametric-types
dense-arrays vs numericals
polymorphic-functions vs ctype
dense-arrays vs py4cl2
polymorphic-functions vs numericals
dense-arrays vs cl-parametric-types
polymorphic-functions vs cffi
dense-arrays vs specialization-store