
import matplotlib.pyplot as plt
import numpy as np

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

		return weights

def    UpperCamelCase_ (	a_  ,				a_  ,				a_  ,				a_		)     ->int:
		A				=weight_matrix(a_  ,				a_  ,				a_		)
		A				=np.linalg.inv(x_train.T @ weight_mat @ x_train		) @ (
		    x_train.T @ weight_mat @ y_train.T
		)

		return weight

def    UpperCamelCase_ (	a_  ,				a_  ,				a_		)     ->List[str]:
		A				=np.zeros(len(a_		)		)  # Initialize array of predictions
		for i, item in enumerate(a_		):
				A				=item @ local_weight(a_  ,				a_  ,				a_  ,				a_		)

		return y_pred

def    UpperCamelCase_ (	a_  ,				a_  ,				a_		)     ->Tuple:
		import seaborn as sns

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

		A				=np.ones(len(a_		)		)

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

		return x_train, x_data, y_data

def    UpperCamelCase_ (	a_  ,				a_  ,				a_  ,				a_  ,				a_  ,				a_  ,				)     ->str:
		A				=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
	__a							  =						load_data("""tips""", """total_bill""", """tip""")
	__a							  =						local_weight_regression(training_data_x, tip, 5)
	plot_preds(training_data_x, predictions, total_bill, tip, """total_bill""", """tip""")











'''simple docstring'''






from .hash_table import HashTable
from .number_theory.prime_numbers import is_prime, next_prime



class  lowerCamelCase__				(						snake_case_						):


	"""simple docstring"""





	def __init__(					self   ,     *UpperCAmelCase__   ,     **UpperCAmelCase__			)       ->				Optional[int]:
					super().__init__(*UpperCAmelCase__   ,     **UpperCAmelCase__			)




	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__			)       ->				Optional[int]:
					_A :				Union[str, Any]				=  (
					    next_prime(value % self.size_table			)
					    if not is_prime(value % self.size_table			)
					    else value % self.size_table
					)  # gt = bigger than
					return next_prime_gt - (data % next_prime_gt)




	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__			)       ->				Union[str, Any]:
					return (increment * self.__hash_function_a(UpperCAmelCase__   ,     UpperCAmelCase__			)) % self.size_table




	def 				_lowerCamelCase				(					self   ,     UpperCAmelCase__   ,     UpperCAmelCase__=None			)       ->				List[Any]:
					_A :				Optional[int]				=  1
					_A :				Optional[int]				=  self.hash_function(UpperCAmelCase__			)

					while self.values[new_key] is not None and self.values[new_key] != key:
									_A :				int				=  (
									    self.__hash_double_function(UpperCAmelCase__   ,     UpperCAmelCase__   ,     UpperCAmelCase__			)
									    if self.balanced_factor() >= self.lim_charge
									    else None
									)
									if new_key is None:
													break
									else:
													i += 1

					return new_key
