SymbolicRegression.jl
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Distributed High-Performance symbolic regression in Julia.
Check out PySR for a Python frontend.
Quickstart
Install in Julia with:
using Pkg
Pkg.add("SymbolicRegression")The heart of this package is the EquationSearch function, which takes a 2D array (shape [features, rows]) and attempts to model a 1D array (shape [rows]) using analytic functional forms.
Run distributed on four processes with:
using SymbolicRegression
X = randn(Float32, 5, 100)
y = 2 * cos.(X[4, :]) + X[1, :] .^ 2 .- 2
options = SymbolicRegression.Options(
binary_operators=(+, *, /, -),
unary_operators=(cos, exp),
npopulations=20
)
hallOfFame = EquationSearch(X, y, niterations=5, options=options, numprocs=4)