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| symreg | AI | |
|---|---|---|
| 4 | 4 | |
| 28 | 14 | |
| - | - | |
| 0.0 | 10.0 | |
| almost 3 years ago | almost 7 years ago | |
| Jupyter Notebook | ||
| MIT License | GNU General Public License v3.0 only |
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symreg
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I Still ‘Lisp’ (and You Should Too)
Well, I wrote a genetic programming library, and it was fun to parse a Lisp-like representation from Python. You still have recursion and everything (albeit no tail call optimization).
Here, `_from_source` goes from a plain array of tokens to a nested one (tree), depending on their arity:
https://github.com/danuker/symreg/blob/7c6593d3046f6c52dfb92...
Lisp is almost valid Python. The exception is the single-element tuple which needs a comma: (x,)
But I still preferred to use Python as a programming language, and Lisp as a sort of AST. It's just easier. I am curious what roadblocks you faced in your ASCII delimited parsing.
Do you by any chance still have the two parsers? I'd love to see them. If you are worried about your anonymity, you can find my website on my HN profile, and my e-mail on my website. I promise not to disclose your identity publicly.
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Do Simpler Machine Learning Models Exist and How Can We Find Them?
If interpretability is sufficiently important, you could straight-up search for mathematical formulae.
My SymReg library pops to mind. I'm thinking of rewriting it in multithreaded Julia this holiday season.
https://github.com/danuker/symreg
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I made an Entity Component System
Indeed, I ran face-first into Python's GIL that prevents any useful CPU-bound multithreading, with my symbolic regression library.
- SymReg: A Python Symbolic Regression Engine
AI
- What's Postgres Got to Do with AI?
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Do Simpler Machine Learning Models Exist and How Can We Find Them?
> similar to genetic programming
A genetical algorithm was also what I was thinking of. Come up with some kind of symbolic (textual) way to represent a wiring/circuit diagram (graph) and evolve the most efficient "learner" using mutation and cross-breeding (e-sex). The earliest GA I read about used Lisp dicing.
As far as "easiest" AI for humans to work with, "Factor tables" may be a way:
https://github.com/RowColz/AI
AI tuning them becomes more like accounting instead of a lab with Doc Brown.
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A Gentle Introduction to Vector Databases
It's kind of like Factor Tables. I'd like to see research projects experimenting with it, rather than just these "garage projects".
What are some alternatives?
SymbolicRegression.jl - Distributed High-Performance Symbolic Regression in Julia
towhee - Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.
FrameworkBenchmarks - Source for the TechEmpower Framework Benchmarks project
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
PyECS - A Python implementation of an Entity Component System
pgsql-http - HTTP client for PostgreSQL, retrieve a web page from inside the database.
coalton - Coalton is an efficient, statically typed functional programming language that supercharges Common Lisp.