



import matplotlib.pyplot as plt
import numpy as np





def     __lowerCamelCase							(   __magic_name__						:	np.ndarray  ,			__magic_name__						:	np.ndarray  ,			__magic_name__						:	float    ):
							a__:							Optional[int]									=len(__snake_case    )  # Number of training samples
							a__:							List[str]									=np.eye(__snake_case    )  # Initialize weights as identity matrix
							for j in range(__snake_case    ):
														a__:							Optional[int]									=point - x_train[j]
														a__:							Tuple									=np.exp(diff @ diff.T / (-2.0 * tau**2)    )

							return weights





def     __lowerCamelCase							(   __magic_name__						:	np.ndarray  ,			__magic_name__						:	np.ndarray  ,			__magic_name__						:	np.ndarray  ,			__magic_name__						:	float    ):
							a__:							Optional[Any]									=weight_matrix(__snake_case  ,			__snake_case  ,			__snake_case    )
							a__:							Optional[Any]									=np.linalg.inv(x_train.T @ weight_mat @ x_train    ) @ (
							    x_train.T @ weight_mat @ y_train.T
							)

							return weight





def     __lowerCamelCase							(   __magic_name__						:	np.ndarray  ,			__magic_name__						:	np.ndarray  ,			__magic_name__						:	float    ):
							a__:							Dict									=np.zeros(len(__snake_case    )    )  # Initialize array of predictions
							for i, item in enumerate(__snake_case    ):
														a__:							Dict									=item @ local_weight(__snake_case  ,			__snake_case  ,			__snake_case  ,			__snake_case    )

							return y_pred





def     __lowerCamelCase							(   __magic_name__						:	str  ,			__magic_name__						:	str  ,			__magic_name__						:	str    ):
							import seaborn as sns

							a__:							List[Any]									=sns.load_dataset(__snake_case    )
							a__:							List[str]									=np.array(data[x_name]    )
							a__:							Optional[Any]									=np.array(data[y_name]    )

							a__:							Optional[int]									=np.ones(len(__snake_case    )    )

							# pairing elements of one and x_data
							a__:							int									=np.column_stack((one, x_data)    )

							return x_train, x_data, y_data





def     __lowerCamelCase							(   __magic_name__						:	np.ndarray  ,			__magic_name__						:	np.ndarray  ,			__magic_name__						:	np.ndarray  ,			__magic_name__						:	np.ndarray  ,			__magic_name__						:	str  ,			__magic_name__						:	str  ,			):
							a__:							Any									=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    	=  load_data('''tips''', '''total_bill''', '''tip''')
		__UpperCAmelCase    	=  local_weight_regression(training_data_x, tip, 5)
		plot_preds(training_data_x, predictions, total_bill, tip, '''total_bill''', '''tip''')









from collections import OrderedDict
from dataclasses import fields
from typing import Any, Tuple

import numpy as np

from .import_utils import is_torch_available
def 				UpperCamelCase_	(					__snake_case      :    Dict  )->   str:




						'''simple docstring'''
						if is_torch_available():
												import torch

												if isinstance(__snake_case						,      torch.Tensor  ):
																		return True

						return isinstance(__snake_case						,      np.ndarray  )




class      __snake_case				(   _UpperCAmelCase):
					def 	a			(						self       :						Optional[Any]  )							->					Optional[Any]:



											'''simple docstring'''





											__magic_name__					=      fields(self  )

											# Safety and consistency checks
											if not len(_lowerCAmelCase  ):
																	raise ValueError(F'''{self.__class__.__name__} has no fields.'''  )

											__magic_name__					=      getattr(self	,      class_fields[0].name  )
											__magic_name__					=      all(getattr(self	,      field.name  ) is None for field in class_fields[1:]  )

											if other_fields_are_none and isinstance(_lowerCAmelCase	,      _lowerCAmelCase  ):
																	for key, value in first_field.items():
																							__magic_name__					=      value
											else:
																	for field in class_fields:
																							__magic_name__					=      getattr(self	,      field.name  )
																							if v is not None:
																													__magic_name__					=      v
					def __delitem__(						self       :						Dict	,      *_lowerCAmelCase       :						List[str]	,      **_lowerCAmelCase       :						List[str]  )							->					List[str]:



											'''simple docstring'''





											raise Exception(F'''You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.'''  )
					def 	a			(						self       :						Optional[int]	,      *_lowerCAmelCase       :						Optional[Any]	,      **_lowerCAmelCase       :						Optional[Any]  )							->					str:



											'''simple docstring'''





											raise Exception(F'''You cannot use ``setdefault`` on a {self.__class__.__name__} instance.'''  )
					def 	a			(						self       :						Union[str, Any]	,      *_lowerCAmelCase       :						List[str]	,      **_lowerCAmelCase       :						Any  )							->					str:



											'''simple docstring'''





											raise Exception(F'''You cannot use ``pop`` on a {self.__class__.__name__} instance.'''  )
					def 	a			(						self       :						Any	,      *_lowerCAmelCase       :						str	,      **_lowerCAmelCase       :						Optional[int]  )							->					List[Any]:



											'''simple docstring'''





											raise Exception(F'''You cannot use ``update`` on a {self.__class__.__name__} instance.'''  )
					def __getitem__(						self       :						Optional[Any]	,      _lowerCAmelCase       :						Any  )							->					Tuple:



											'''simple docstring'''





											if isinstance(_lowerCAmelCase	,      _lowerCAmelCase  ):
																	__magic_name__					=      dict(self.items()  )
																	return inner_dict[k]
											else:
																	return self.to_tuple()[k]
					def __setattr__(						self       :						str	,      _lowerCAmelCase       :						Optional[Any]	,      _lowerCAmelCase       :						Optional[int]  )							->					int:



											'''simple docstring'''





											if name in self.keys() and value is not None:
																	# Don't call self.__setitem__ to avoid recursion errors
																	super().__setitem__(_lowerCAmelCase	,      _lowerCAmelCase  )
											super().__setattr__(_lowerCAmelCase	,      _lowerCAmelCase  )
					def __setitem__(						self       :						Optional[Any]	,      _lowerCAmelCase       :						Optional[Any]	,      _lowerCAmelCase       :						List[str]  )							->					Optional[int]:



											'''simple docstring'''





											super().__setitem__(_lowerCAmelCase	,      _lowerCAmelCase  )
											# Don't call self.__setattr__ to avoid recursion errors
											super().__setattr__(_lowerCAmelCase	,      _lowerCAmelCase  )







					def 	a			(						self       :						str  )							->					Tuple[Any]:



											'''simple docstring'''





											return tuple(self[k] for k in self.keys()  )

