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