


import matplotlib.pyplot as plt
import numpy as np
def 				UpperCamelCase_	(					__snake_case      :    np.ndarray						,      __snake_case      :    np.ndarray						,      __snake_case      :    float  )->   np.ndarray:




						'''simple docstring'''
						__magic_name__					=      len(__snake_case  )  # Number of training samples
						__magic_name__					=      np.eye(__snake_case  )  # Initialize weights as identity matrix
						for j in range(__snake_case  ):
												__magic_name__					=      point - x_train[j]
												__magic_name__					=      np.exp(diff @ diff.T / (-2.0 * tau**2)  )

						return weights
def 				UpperCamelCase_	(					__snake_case      :    np.ndarray						,      __snake_case      :    np.ndarray						,      __snake_case      :    np.ndarray						,      __snake_case      :    float  )->   np.ndarray:




						'''simple docstring'''
						__magic_name__					=      weight_matrix(__snake_case						,      __snake_case						,      __snake_case  )
						__magic_name__					=      np.linalg.inv(x_train.T @ weight_mat @ x_train  ) @ (
						    x_train.T @ weight_mat @ y_train.T
						)

						return weight
def 				UpperCamelCase_	(					__snake_case      :    np.ndarray						,      __snake_case      :    np.ndarray						,      __snake_case      :    float  )->   np.ndarray:




						'''simple docstring'''
						__magic_name__					=      np.zeros(len(__snake_case  )  )  # Initialize array of predictions
						for i, item in enumerate(__snake_case  ):
												__magic_name__					=      item @ local_weight(__snake_case						,      __snake_case						,      __snake_case						,      __snake_case  )

						return y_pred
def 				UpperCamelCase_	(					__snake_case      :    str						,      __snake_case      :    str						,      __snake_case      :    str  )->   tuple[np.ndarray, np.ndarray, np.ndarray]:




						'''simple docstring'''
						import seaborn as sns

						__magic_name__					=      sns.load_dataset(__snake_case  )
						__magic_name__					=      np.array(data[x_name]  )
						__magic_name__					=      np.array(data[y_name]  )

						__magic_name__					=      np.ones(len(__snake_case  )  )

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

						return x_train, x_data, y_data
def 				UpperCamelCase_	(					__snake_case      :    np.ndarray						,      __snake_case      :    np.ndarray						,      __snake_case      :    np.ndarray						,      __snake_case      :    np.ndarray						,      __snake_case      :    str						,      __snake_case      :    str						,      )->   None:




						'''simple docstring'''
						__magic_name__					=      np.sort(__snake_case						,      axis=0  )
						plt.scatter(__snake_case						,      __snake_case						,      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(__snake_case  )
						plt.ylabel(__snake_case  )
						plt.show()


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					# Demo with a dataset from the seaborn module
					UpperCamelCase     ,UpperCamelCase     ,UpperCamelCase			:					Tuple         =							load_data('''tips''', '''total_bill''', '''tip''')
					UpperCamelCase			:					List[str]         =							local_weight_regression(training_data_x, tip, 5)
					plot_preds(training_data_x, predictions, total_bill, tip, '''total_bill''', '''tip''')





from ..utils import DummyObject, requires_backends

class      __snake_case				(   metaclass=_UpperCAmelCase):
					SCREAMING_SNAKE_CASE_		:       str								=			["torch", "transformers", "k_diffusion"]
					def __init__(						self       :						Tuple	,      *_lowerCAmelCase       :						Union[str, Any]	,      **_lowerCAmelCase       :						Union[str, Any]  )							->					Dict:



											'''simple docstring'''





											requires_backends(self	,      ['torch', 'transformers', 'k_diffusion']  )
					@classmethod
					def 	a			(						cls       :						Optional[int]	,      *_lowerCAmelCase       :						Any	,      **_lowerCAmelCase       :						Optional[Any]  )							->					Optional[Any]:



											'''simple docstring'''





											requires_backends(cls	,      ['torch', 'transformers', 'k_diffusion']  )







					@classmethod
					def 	a			(						cls       :						Union[str, Any]	,      *_lowerCAmelCase       :						Tuple	,      **_lowerCAmelCase       :						str  )							->					Tuple:



											'''simple docstring'''





											requires_backends(cls	,      ['torch', 'transformers', 'k_diffusion']  )

