




"""simple docstring"""



import matplotlib.pyplot as plt
import numpy as np







def 						_SCREAMING_SNAKE_CASE     (							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 						_SCREAMING_SNAKE_CASE     (							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 						_SCREAMING_SNAKE_CASE     (							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 						_SCREAMING_SNAKE_CASE     (							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 						_SCREAMING_SNAKE_CASE     (							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
      SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE       = load_data("tips", "total_bill", "tip")
      SCREAMING_SNAKE_CASE       = local_weight_regression(training_data_x, tip, 5)
      plot_preds(training_data_x, predictions, total_bill, tip, "total_bill", "tip")











"""simple docstring"""



from transformers import PretrainedConfig




class 			UpperCAmelCase_						(		A_							):
    lowercase__		      =   '''custom'''




    def __init__(					self		: Any			,				snake_case_		: List[Any]=1			,				**snake_case_		: Any   )   ->							str:




     '''simple docstring'''

     A__ =       attribute
     super().__init__(**snake_case_   )






class 			UpperCAmelCase_						(		A_							):
    lowercase__		      =   '''custom'''




    def __init__(					self		: Any			,				snake_case_		: Tuple=1			,				**snake_case_		: Dict   )   ->							Any:




     '''simple docstring'''

     A__ =       attribute





