SymbolicRegressionPackage

Wolfram Mathematica package for brute-force symbolic regression: exhaustive search over expression trees built from user-specified constants, unary functions, and binary operations.

Core functionality

• RecognizeConstant[x] — identify an analytic formula for a numeric value, searching in order of growing Kolmogorov complexity.
• RecognizeFunction, RecognizeSequence — same idea for univariate data and integer sequences.
• RandomExpression[], EnumerateExpressions[] — generate random or exhaustive expression trees from given building blocks.
• VerifyBaseSet[constants, functions, operations] — bootstrapping completeness check: given a set of primitives, verify whether elementary functions can be reconstructed from given primitives.

Quick start

Check out examples from SymbolicRegressionPackage_Examples.nb.

Repository structure

• SymbolicRegression.m — main Mathematica package
• EML_toolkit/ — EML compiler (Python), numerical test harnesses (C, NumPy, PyTorch, mpmath), symbolic verification notebooks, PyTorch tree trainer, figure scripts, and CUDA shortest-expression search tools
• rust_*/ — Rust reimplementation of the bootstrapping procedure and search tools (~35 s vs ~40 min in Mathematica)

Requirements

• Wolfram Mathematica (≥13.0)
• Python ≥3.9 with NumPy, PyTorch, mpmath (for EML toolkit)
• Rust ≥1.70 (for rust tools)
• C compiler (for Eml_verify)
• CUDA toolkit (for EmL_recognizer)

License

MIT