# SymbolicRegression.jl

Latest releaseDocumentationBuild statusCoverage     Distributed High-Performance symbolic regression in Julia.

Check out PySR for a Python frontend.

<img src="https://astroautomata.com/data/srdemoimage1.png" alt="demo1" width="700"/> <img src="https://astroautomata.com/data/srdemoimage2.png" alt="demo2" width="700"/>

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# 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)

We can view the equations in the dominating Pareto frontier with:

dominating = calculateParetoFrontier(X, y, hallOfFame, options)

We can convert the best equation to SymbolicUtils.jl with the following function:

eqn = node_to_symbolic(dominating[end].tree, options)
println(simplify(eqn*5 + 3))

We can also print out the full pareto frontier like so:

println("Complexity\tMSE\tEquation")

for member in dominating
size = countNodes(member.tree)
score = member.score
string = stringTree(member.tree, options)

println("$(size)\t$(score)\t\$(string)")
end