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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 | |
loss="loss(x, y) = (x - y)^2", | |
# ^ Custom loss function (julia syntax) | |
) | |
model.fit(X, y) | |
print(model) | |