High-Performance Symbolic Regression in Python and Julia
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Updated
Jun 3, 2024 - Python
High-Performance Symbolic Regression in Python and Julia
Physical Symbolic Optimization
Genetic Programming in Python, with a scikit-learn inspired API
Distributed High-Performance Symbolic Regression in Julia
Generating sets of formulaic alpha (predictive) stock factors via reinforcement learning.
A data-driven method combining symbolic regression and compressed sensing for accurate & interpretable models.
A framework for gene expression programming (an evolutionary algorithm) in Python
C++ Large Scale Genetic Programming
Symbolic regression solver, based on genetic programming methodology.
SymbolicNumericIntegration.jl: Symbolic-Numerics for Solving Integrals
a python 3 library based on deap providing abstraction layers for symbolic regression problems.
Ridiculously fast symbolic expressions
EC-KitY is a scikit-learn-compatible Python tool kit for doing evolutionary computation.
Automatic equation building and curve fitting. Runs on Tensorflow. Built for academia and research.
Official repository for the paper "Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery"
🔮 Symbolic regression library
predicting equations from raw data with deep learning
Codebase for "Demystifying Black-box Models with Symbolic Metamodels", NeurIPS 2019.
Genetic Programming version of GOMEA. Also includes standard tree-based GP, and Semantic Backpropagation-based GP
Symbolic regression is the task of identifying a mathematical expression that best fits a provided dataset of input and output values. In this work, we present SymbolicGPT, a novel transformer-based language model for symbolic regression.
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