"""simple docstring"""





import matplotlib.pyplot as plt
import numpy as np





def     a_					(				_lowerCAmelCase  :			np.ndarray       ,			_lowerCAmelCase  :			np.ndarray       ,			_lowerCAmelCase  :			float					):





       '''simple docstring'''

       lowercase__      :		Optional[int]          =     len(_lowerCAmelCase					)  # Number of training samples
       lowercase__      :		List[str]          =     np.eye(_lowerCAmelCase					)  # Initialize weights as identity matrix
       for j in range(_lowerCAmelCase					):
              lowercase__      :		str          =     point - x_train[j]
              lowercase__      :		Union[str, Any]          =     np.exp(diff @ diff.T / (-2.0 * tau**2)					)

       return weights





def     a_					(				_lowerCAmelCase  :			np.ndarray       ,			_lowerCAmelCase  :			np.ndarray       ,			_lowerCAmelCase  :			np.ndarray       ,			_lowerCAmelCase  :			float					):





       '''simple docstring'''

       lowercase__      :		List[str]          =     weight_matrix(_lowerCAmelCase       ,			_lowerCAmelCase       ,			_lowerCAmelCase					)
       lowercase__      :		List[str]          =     np.linalg.inv(x_train.T @ weight_mat @ x_train					) @ (
           x_train.T @ weight_mat @ y_train.T
       )

       return weight





def     a_					(				_lowerCAmelCase  :			np.ndarray       ,			_lowerCAmelCase  :			np.ndarray       ,			_lowerCAmelCase  :			float					):





       '''simple docstring'''

       lowercase__      :		Any          =     np.zeros(len(_lowerCAmelCase					)					)  # Initialize array of predictions
       for i, item in enumerate(_lowerCAmelCase					):
              lowercase__      :		Union[str, Any]          =     item @ local_weight(_lowerCAmelCase       ,			_lowerCAmelCase       ,			_lowerCAmelCase       ,			_lowerCAmelCase					)

       return y_pred





def     a_					(				_lowerCAmelCase  :			str       ,			_lowerCAmelCase  :			str       ,			_lowerCAmelCase  :			str					):





       '''simple docstring'''

       import seaborn as sns

       lowercase__      :		Optional[int]          =     sns.load_dataset(_lowerCAmelCase					)
       lowercase__      :		int          =     np.array(data[x_name]					)
       lowercase__      :		int          =     np.array(data[y_name]					)

       lowercase__      :		List[Any]          =     np.ones(len(_lowerCAmelCase					)					)

       # pairing elements of one and x_data
       lowercase__      :		Any          =     np.column_stack((one, x_data)					)

       return x_train, x_data, y_data





def     a_					(				_lowerCAmelCase  :			np.ndarray       ,			_lowerCAmelCase  :			np.ndarray       ,			_lowerCAmelCase  :			np.ndarray       ,			_lowerCAmelCase  :			np.ndarray       ,			_lowerCAmelCase  :			str       ,			_lowerCAmelCase  :			str       ,			):





       '''simple docstring'''

       lowercase__      :		Tuple          =     np.sort(_lowerCAmelCase       ,			axis=0					)
       plt.scatter(_lowerCAmelCase       ,			_lowerCAmelCase       ,			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(_lowerCAmelCase					)
       plt.ylabel(_lowerCAmelCase					)
       plt.show()


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       # Demo with a dataset from the seaborn module
       _UpperCamelCase   ,				_UpperCamelCase   ,				_UpperCamelCase       :						Optional[int]     =   load_data("tips", "total_bill", "tip")
       _UpperCamelCase       :						Optional[Any]     =   local_weight_regression(training_data_x, tip, 5)
       plot_preds(training_data_x, predictions, total_bill, tip, "total_bill", "tip")


"""simple docstring"""





from ..utils import DummyObject, requires_backends


class 					UpperCAmelCase_     (   metaclass=_a):
      lowerCamelCase__		:   List[str]			  =  ["torch", "transformers", "k_diffusion"]


      def __init__(       self		,      *a		,      **a							)	-> Dict:
             requires_backends(self		,      ['torch', 'transformers', 'k_diffusion']							)


      @classmethod
      def 		_UpperCAmelCase	(       cls		,      *a		,      **a							)	-> Tuple:
             requires_backends(cls		,      ['torch', 'transformers', 'k_diffusion']							)


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

