


"""simple docstring"""


import matplotlib.pyplot as plt
import numpy as np






def   a__			(					__lowercase  ,      __lowercase  ,      __lowercase )						->						np.ndarray:
				_A       	=      len(__lowercase )  # Number of training samples
				_A       	=      np.eye(__lowercase )  # Initialize weights as identity matrix
				for j in range(__lowercase ):
								_A       	=      point - x_train[j]
								_A       	=      np.exp(diff @ diff.T / (-2.0 * tau**2) )

				return weights






def   a__			(					__lowercase  ,      __lowercase  ,      __lowercase  ,      __lowercase )						->						np.ndarray:
				_A       	=      weight_matrix(__lowercase  ,      __lowercase  ,      __lowercase )
				_A       	=      np.linalg.inv(x_train.T @ weight_mat @ x_train ) @ (
				    x_train.T @ weight_mat @ y_train.T
				)

				return weight






def   a__			(					__lowercase  ,      __lowercase  ,      __lowercase )						->						np.ndarray:
				_A       	=      np.zeros(len(__lowercase ) )  # Initialize array of predictions
				for i, item in enumerate(__lowercase ):
								_A       	=      item @ local_weight(__lowercase  ,      __lowercase  ,      __lowercase  ,      __lowercase )

				return y_pred






def   a__			(					__lowercase  ,      __lowercase  ,      __lowercase )						->						tuple[np.ndarray, np.ndarray, np.ndarray]:
				import seaborn as sns

				_A       	=      sns.load_dataset(__lowercase )
				_A       	=      np.array(data[x_name] )
				_A       	=      np.array(data[y_name] )

				_A       	=      np.ones(len(__lowercase ) )

				# pairing elements of one and x_data
				_A       	=      np.column_stack((one, x_data) )

				return x_train, x_data, y_data






def   a__			(					__lowercase  ,      __lowercase  ,      __lowercase  ,      __lowercase  ,      __lowercase  ,      __lowercase  ,      )						->						None:
				_A       	=      np.sort(__lowercase  ,      axis=0 )
				plt.scatter(__lowercase  ,      __lowercase  ,      color="blue" )
				plt.plot(
				    x_train_sorted[:, 1]  ,      preds[x_train[:, 1].argsort(0 )]  ,      color="yellow"  ,      linewidth=5  ,      )
				plt.title("Local Weighted Regression" )
				plt.xlabel(__lowercase )
				plt.ylabel(__lowercase )
				plt.show()


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		# Demo with a dataset from the seaborn module
		a_				,  a_				,  a_											=     load_data("tips", "total_bill", "tip")
		a_											=     local_weight_regression(training_data_x, tip, 5)
		plot_preds(training_data_x, predictions, total_bill, tip, "total_bill", "tip")



"""simple docstring"""


from .hash_table import HashTable
class snake_case   (	_UpperCamelCase):







		def __init__(       self   :							Any     , *a__   :							Any     , **a__   :							Tuple		)							->    Tuple:





						'''simple docstring'''







						super().__init__(*a__     , **a__		)







		def 			a_			(       self   :							Tuple     , a__   :							int     , a__   :							str=None		)							->    Optional[int]:





						'''simple docstring'''







						_A       	=      1
						_A       	=      self.hash_function(key + i * i		)

						while self.values[new_key] is not None and self.values[new_key] != key:
										i += 1
										_A       	=      (
										    self.hash_function(key + i * i		)
										    if not self.balanced_factor() >= self.lim_charge
										    else None
										)

										if new_key is None:
														break

						return new_key