3/4/2024 0 Comments Xlag 4.0predict ( x_valid, y_valid ) > rrse = root_relative_squared_error ( y_valid, yhat ) > print ( rrse ) 0.001993603325328823 > r = pd. fit ( x_train, y_train ) > yhat = model. train_percentage = 90 ) > basis_function = Polynomial ( degree = 2 ) > model = PolynomialNarmax ( basis_function = basis_function. > import numpy as np > import matplotlib.pyplot as plt > from sysidentpy.model_structure_selection import FROLS > from sysidentpy.basis_function._basis_function import Polynomial > from _results import results > from trics import root_relative_squared_error > from _data import get_miso_data, get_siso_data > x_train, x_valid, y_train, y_valid = get_siso_data ( n = 1000.
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