code: 
  
  
  
  
  """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")
  
  
  
  
  
  
style_context: 
  
  
  
  
  """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
  
  
  
  
  
  