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PySR Alternatives
Similar projects and alternatives to PySR
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ModelingToolkit.jl
An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations
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diffeqpy
Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization
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Nutrient
Nutrient – The #1 PDF SDK Library, trusted by 10K+ developers. Other PDF SDKs promise a lot - then break. Laggy scrolling, poor mobile UX, tons of bugs, and lack of support cost you endless frustrations. Nutrient’s SDK handles billion-page workloads - so you don’t have to debug PDFs. Used by ~1 billion end users in more than 150 different countries.
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GeneticAlgorithmPython
Source code of PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).
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mljar-supervised
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
PySR discussion
PySR reviews and mentions
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Genetically synthesized supergain broadband wire-bundle antenna
For those who're only distantly aware of the kind of problem this solves (like me), the wikipedia link further elaborates:
https://en.wikipedia.org/wiki/Symbolic_regression
and turns out there's a Python package
https://github.com/MilesCranmer/PySR
I've needed something like this at least once (but IIRC no more than twice ;) ), so I'm glad to know what to look for next time, thanks for the rabbit hole!
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Potential of the Julia programming language for high energy physics computing
> Yes, julia can be called from other languages rather easily
This seems false to me. StaticCompiler.jl [1] puts in their limitations that "GC-tracked allocations and global variables do not work with compile_executable or compile_shlib. This has some interesting consequences, including that all functions within the function you want to compile must either be inlined or return only native types (otherwise Julia would have to allocate a place to put the results, which will fail)." PackageCompiler.jl [2] has the same limitations if I'm not mistaken. So then you have to fall back to distributing the Julia "binary" with a full Julia runtime, which is pretty heavy. There are some packages which do this. For example, PySR [3] does this.
There is some word going around though that there is an even better static compiler in the making, but as long as that one is not publicly available I'd say that Julia cannot easily be called from other languages.
[1]: https://github.com/tshort/StaticCompiler.jl
[2]: https://github.com/JuliaLang/PackageCompiler.jl
[3]: https://github.com/MilesCranmer/PySR
- Symbolicregression.jl – High-Performance Symbolic Regression in Julia and Python
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[D] Is there any research into using neural networks to discover classical algorithms?
I first learned about it with PySR https://github.com/MilesCranmer/PySR, they have an arxiv paper with some use cases as well.
- Symbolic Regression is NP-hard
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‘Machine Scientists’ Distill the Laws of Physics from Raw Data
I found it curious that one of the implementations of symbolic regression (the "machine scientist" referenced in the article) is a Python wrapper on Julia: https://github.com/MilesCranmer/PySR
I don't think I've seen a Python wrapper on Julia code before.
- Is it possible to create a Python package with Julia and publish it on PyPi?
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[D] Inferring general physical laws from observations in 300 lines of code
This is really neat! Since you're interested in this subject, you may also appreciate PySR and the corresponding paper which uses Graph Neural Networks to perform symbolic regression.
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MilesCranmer/PySR is an open source project licensed under Apache License 2.0 which is an OSI approved license.
The primary programming language of PySR is Python.