




import matplotlib.pyplot as plt
import numpy as np





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     np.ndarray:







				'''simple docstring'''


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

				return weights





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     np.ndarray:







				'''simple docstring'''


				__lowerCAmelCase					=  weight_matrix(UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )
				__lowerCAmelCase					=  np.linalg.inv(x_train.T @ weight_mat @ x_train   ) @ (
				    x_train.T @ weight_mat @ y_train.T
				)

				return weight





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     np.ndarray:







				'''simple docstring'''


				__lowerCAmelCase					=  np.zeros(len(UpperCamelCase__   )   )  # Initialize array of predictions
				for i, item in enumerate(UpperCamelCase__   ):
								__lowerCAmelCase					=  item @ local_weight(UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )

				return y_pred





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__   )     ->     tuple[np.ndarray, np.ndarray, np.ndarray]:







				'''simple docstring'''


				import seaborn as sns

				__lowerCAmelCase					=  sns.load_dataset(UpperCamelCase__   )
				__lowerCAmelCase					=  np.array(data[x_name]   )
				__lowerCAmelCase					=  np.array(data[y_name]   )

				__lowerCAmelCase					=  np.ones(len(UpperCamelCase__   )   )

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

				return x_train, x_data, y_data





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			)     ->     None:







				'''simple docstring'''


				__lowerCAmelCase					=  np.sort(UpperCamelCase__						,			axis=0   )
				plt.scatter(UpperCamelCase__						,			UpperCamelCase__						,			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(UpperCamelCase__   )
				plt.ylabel(UpperCamelCase__   )
				plt.show()


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	# Demo with a dataset from the seaborn module
	__A    ,    __A    ,    __A						:		Dict          =						load_data("tips", "total_bill", "tip")
	__A						:		Optional[int]          =						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 		lowercase_       (	metaclass=lowerCAmelCase__				):
					__UpperCamelCase     	= ["torch", "transformers", "k_diffusion"]




					def __init__(   self:		Tuple,     *_lowercase:		Dict,     **_lowercase:		Optional[int]):


									'''simple docstring'''


									requires_backends(self,     ["""torch""", """transformers""", """k_diffusion"""])




					@classmethod
					def     _lowercase						(   cls:		Union[str, Any],     *_lowercase:		Union[str, Any],     **_lowercase:		Optional[int]):


									'''simple docstring'''


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







					@classmethod
					def     _lowercase						(   cls:		List[str],     *_lowercase:		int,     **_lowercase:		List[Any]):


									'''simple docstring'''


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





