https://github.com/timm/shortr
A little lab of learning algorithms (in LUA). Less XAI, but better
https://github.com/timm/shortr
ai clustering data-mining decision-trees explanation less-is-more lua multi-objective-optimization naive-bayes-classifier nearest-neighbors random-projection semi-supervised-learning teaching xai
Last synced: about 2 months ago
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A little lab of learning algorithms (in LUA). Less XAI, but better
- Host: GitHub
- URL: https://github.com/timm/shortr
- Owner: timm
- License: other
- Created: 2019-09-04T01:40:41.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2022-10-01T23:14:04.000Z (over 2 years ago)
- Last Synced: 2023-07-31T08:10:28.445Z (almost 2 years ago)
- Topics: ai, clustering, data-mining, decision-trees, explanation, less-is-more, lua, multi-objective-optimization, naive-bayes-classifier, nearest-neighbors, random-projection, semi-supervised-learning, teaching, xai
- Language: HTML
- Homepage: http://menzies.us/shortr
- Size: 29.2 MB
- Stars: 4
- Watchers: 2
- Forks: 3
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff
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README
# shortr.lua:
less, but better XAI. :eyes:
Semi-supervised multi-objective optimization XAI.
From N items, find and explain the best ones, using just log(N) evals.
> __Dieter Rams:__
_"Less, but better."_
> __Alan Perlis:__
_"Simplicity does not precede complexity, but follows it."_
> __Tony Hoare:__
_"Inside every large program is a small program struggling to get out."_
Now that you've done _it_, did you really understand _it_? Let's check.
Can you do _it_ better?
Can you now
write _it_ in fewer lines and do you know how to make _it_ run faster?
Can you see how _it_ is same/different to other things?
And can you use those similarities to do more things with _it_?
Finally, can you teach _it_ quickly to newcomers?
E.g. do I understand a multi-objective semi-supervised XAI?
Well, maybe. Here's all that, most of which is
background stuff that could be used for other learners.
Once
I build that, I found that decision trees, Naive Bayes classifiers,
and nearest neighbors were all tiny extensions.
Also included here
is literate programming, self-documenting code and support for
test-driven development.
All in a few hundreds lines of LUA:
`awk '!/^(--|[ \t]*$)/{n++} END {print n" lines"}' *.lua` => 511 lines
Share and enjoy.
### Coding conventions
Before reading this code, it might be best to [review these local coding conventions](https://github.com/timm/shortr/blob/master/CONTRIBUTE.md).
And why, you may ask, is this written in LUA? Well. the whole point of this code is make people say "hey, that
is so easy I can write it in Rust, Typescript, Python, Julia, Erlang, Ruby, etc, etc...". So this code steers
clear of clever coding practices
that might not easily port. Off-the-shelf LUA has less than 2 dozen built-ins-- which makes it pretty useful as a simple executable specification system.
(Aside: also, for me, LUA is kinda fun to use.)
[Jack Diederich](https://www.youtube.com/watch?v=o9pEzgHorH0)
, [Hilary Mason](https://www.youtube.com/watch?v=l2btv0yUPNQ)
, [Brian McFee](https://brianmcfee.net/papers/ismir2011_sptree.pdf)
, [Brian Kernighan](https://www.oreilly.com/library/view/beautiful-code/9780596510046/ch01.html)
, [Joel Grus](https://github.com/joelgrus/data-science-from-scratch).
Especially the LISPers:
([Peter Seibel](https://gigamonkeys.com/book/)
([Conrad Barski](https://doc.lagout.org/programmation/Lisp/Land%20of%20Lisp_%20Learn%20to%20Program%20in%20Lisp%2C%20One%20Game%20at%20a%20Time%20%5BBarski%202010-11-15%5D.pdf)
([Paul Graham](http://www.paulgraham.com/onlisp.html)
([Peter Norvig](http://norvig.com/lispy.html)
([Guy Steele](https://dspace.mit.edu/bitstream/handle/1721.1/5790/AIM-353.pdf?sequence=2&isAllowed=y)))))).
### Role Models
People that inspire me to code less, but better:
[Jack Diederich](https://www.youtube.com/watch?v=o9pEzgHorH0)
, [Hilary Mason](https://www.youtube.com/watch?v=l2btv0yUPNQ)
, [Brian Kernighan](https://www.oreilly.com/library/view/beautiful-code/9780596510046/ch01.html)
, [Joel Grus](https://github.com/joelgrus/data-science-from-scratch)
, [Brian McFee](https://brianmcfee.net/papers/ismir2011_sptree.pdf).
Especially the LISPers:
([Peter Seibel](https://gigamonkeys.com/book/)
([Conrad Barski](https://doc.lagout.org/programmation/Lisp/Land%20of%20Lisp_%20Learn%20to%20Program%20in%20Lisp%2C%20One%20Game%20at%20a%20Time%20%5BBarski%202010-11-15%5D.pdf)
([Paul Graham](http://www.paulgraham.com/onlisp.html)
([Peter Norvig](http://norvig.com/lispy.html)
([Guy Steele](https://dspace.mit.edu/bitstream/handle/1721.1/5790/AIM-353.pdf?sequence=2&isAllowed=y)))))).