example.py

import numpy as np

X = 2 * np.random.randn(100, 5)
y = 2.5382 * np.cos(X[:, 3]) + X[:, 0] ** 2 - 0.5

from pysr import PySRRegressor

model = PySRRegressor(
    model_selection="best",  # Result is mix of simplicity+accuracy
    niterations=40,
    binary_operators=["+", "*"],
    unary_operators=[
        "cos",
        "exp",
        "sin",
        "inv(x) = 1/x",
        # ^ Custom operator (julia syntax)
    ],
    extra_sympy_mappings={"inv": lambda x: 1 / x},
    # ^ Define operator for SymPy as well
    elementwise_loss="loss(x, y) = (x - y)^2",
    # ^ Custom loss function (julia syntax)
)

model.fit(X, y)

print(model)