jelm VS prechelt-phone-number-enco

I have a bunch of links to ML material for either APL or J. I don't know of any particular library for J. J is interpreted, so it is not as fast as other implementations. I am mainly using it to experiment on concepts and teach myself more ML in J because of the iterative nature of the REPL, and the succinct code. I can keep what's going on in my head, and glance at less than 100 lines, usually 15 lines, of code to refresh it.

There is a series of videos of learning neural networks in APL cited by others here on this thread.

Pandas author, Wes McKinney, cited J as an influence in his work on Pandas.

Extreme Learning Machine in J (code and PDF are here too):

https://github.com/peportier/jelm

Convolutional neural networks in APL (PDF and video on page):

https://dl.acm.org/doi/10.1145/3315454.3329960

A DSL to implement MENACE (Matchbox Educable Noughts And Crosses Engine) in APL (Noughts and Crosses or Tic-tac-toe):

https://romilly.github.io/o-x-o/an-introduction.html

> it’s using Unicode-aware string stuff

Rust uses UTF-8 internally for Strings, so it's very efficient to parse a file into a String, then using slices to go through it... this is probably the best you can get as parsing ASCII input as UTF-8 is very efficient (the 0-bit is always zero in ASCII, the unicode decoder only needs to check that's the case for every byte, so it's not some kind of complicated computation it's doing to decode)...

If you use bytes for everything, you will make the whole code much harder to follow and it still won't run faster.

Check for yourself: https://github.com/renatoathaydes/prechelt-phone-number-enco...

This is a pretty introductory CL article, mostly a commentary on Norvig's solution to the problem. Still, I learned about the #. readmacro from it. The conclusion: "[The Lisp implementation] was the fastest implementation for all input sizes except the largest one, where it performed just slightly worse than my best Java implementation." GH repo at https://github.com/renatoathaydes/prechelt-phone-number-enco.... Sounds like he was mostly measuring the performance of the SBCL bignum implementation.

source: https://github.com/renatoathaydes/prechelt-phone-number-enco...