





class 	_UpperCamelCase	:







					'''simple docstring'''

					def __init__(    self     :	Optional[int]		):






						'''simple docstring'''







						UpperCAmelCase_		    =  {}
						# indexmap keys are an index and it's values are the number at that index
						UpperCAmelCase_		    =  {}





					def 							SCREAMING_SNAKE_CASE				(    self     :	Tuple			,       __lowercase     :	list | str | range			,       __lowercase     :	int		):






						'''simple docstring'''







						if isinstance(__lowercase			,       (range, str)		):
							UpperCAmelCase_		    =  list(__lowercase		)
						elif not isinstance(__lowercase			,       __lowercase		):
							raise TypeError(
							    """binary_search_delete() only accepts either a list, range or str"""		)

						UpperCAmelCase_		    =  0
						UpperCAmelCase_		    =  len(__lowercase		) - 1

						while low <= high:
							UpperCAmelCase_		    =  (low + high) // 2
							if array[mid] == item:
								array.pop(__lowercase		)
								return array
							elif array[mid] < item:
								UpperCAmelCase_		    =  mid + 1
							else:
								UpperCAmelCase_		    =  mid - 1
						raise ValueError(
						    """Either the item is not in the array or the array was unsorted"""		)





					def 							SCREAMING_SNAKE_CASE				(    self     :	List[Any]			,       __lowercase     :	list | str | range			,       __lowercase     :	int		):






						'''simple docstring'''







						if isinstance(__lowercase			,       (range, str)		):
							UpperCAmelCase_		    =  list(__lowercase		)
						elif not isinstance(__lowercase			,       __lowercase		):
							raise TypeError(
							    """binary_search_insert() only accepts either a list, range or str"""		)

						UpperCAmelCase_		    =  0
						UpperCAmelCase_		    =  len(__lowercase		) - 1

						while low <= high:
							UpperCAmelCase_		    =  (low + high) // 2
							if array[mid] == index:
								# If the item already exists in the array,
								# insert it after the existing item
								array.insert(mid + 1			,       __lowercase		)
								return array
							elif array[mid] < index:
								UpperCAmelCase_		    =  mid + 1
							else:
								UpperCAmelCase_		    =  mid - 1

        # If the item doesn't exist in the array, insert it at the appropriate position
						array.insert(__lowercase			,       __lowercase		)
						return array





					def 							SCREAMING_SNAKE_CASE				(    self     :	Optional[Any]			,       __lowercase     :	int			,       __lowercase     :	int		):






						'''simple docstring'''







						if index in self.indexmap:
							UpperCAmelCase_		    =  self.indexmap[index]
							if len(self.numbermap[n]		) == 1:
								del self.numbermap[n]
							else:
								UpperCAmelCase_		    =  self.binary_search_delete(self.numbermap[n]			,       __lowercase		)

        # Set new index
						UpperCAmelCase_		    =  number

						# Number not seen before or empty so insert number value
						if number not in self.numbermap:
							UpperCAmelCase_		    =  [index]

						# Here we need to perform a binary search insertion in order to insert
						# The item in the correct place
						else:
							UpperCAmelCase_		    =  self.binary_search_insert(
							    self.numbermap[number]			,       __lowercase		)





					def 							SCREAMING_SNAKE_CASE				(    self     :	Tuple			,       __lowercase     :	int		):






						'''simple docstring'''







						return self.numbermap.get(__lowercase			,       [-1]		)[0]


if __name__ == "__main__":
				import doctest

				doctest.testmod()









import matplotlib.pyplot as plt
import numpy as np




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

	return weights




def  A_(      A					,   A					,   A					,   A ):
	UpperCAmelCase_		    =  weight_matrix(A					,   A					,   A )
	UpperCAmelCase_		    =  np.linalg.inv(x_train.T @ weight_mat @ x_train ) @ (
	    x_train.T @ weight_mat @ y_train.T
	)

	return weight




def  A_(      A					,   A					,   A ):
	UpperCAmelCase_		    =  np.zeros(len(A ) )  # Initialize array of predictions
	for i, item in enumerate(A ):
		UpperCAmelCase_		    =  item @ local_weight(A					,   A					,   A					,   A )

	return y_pred




def  A_(      A					,   A					,   A ):
	import seaborn as sns

	UpperCAmelCase_		    =  sns.load_dataset(A )
	UpperCAmelCase_		    =  np.array(data[x_name] )
	UpperCAmelCase_		    =  np.array(data[y_name] )

	UpperCAmelCase_		    =  np.ones(len(A ) )

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

	return x_train, x_data, y_data




def  A_(      A					,   A					,   A					,   A					,   A					,   A					,   ):
	UpperCAmelCase_		    =  np.sort(A					,   axis=0 )
	plt.scatter(A					,   A					,   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(A )
	plt.ylabel(A )
	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__ :			Tuple       	=			local_weight_regression(training_data_x, tip, 5)
				plot_preds(training_data_x, predictions, total_bill, tip, """total_bill""", """tip""")


