



'''simple docstring'''
import matplotlib.pyplot as plt
import numpy as np



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

			return weights



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      np.ndarray							,							lowerCamelCase__   :      np.ndarray							,							lowerCamelCase__   :      np.ndarray							,							lowerCamelCase__   :      float				):
			A:      Dict														=   weight_matrix(lowerCamelCase__							,							lowerCamelCase__							,							lowerCamelCase__				)
			A:      Dict														=   np.linalg.inv(x_train.T @ weight_mat @ x_train				) @ (
			    x_train.T @ weight_mat @ y_train.T
			)

			return weight



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      np.ndarray							,							lowerCamelCase__   :      np.ndarray							,							lowerCamelCase__   :      float				):
			A:      Tuple														=   np.zeros(len(lowerCamelCase__				)				)  # Initialize array of predictions
			for i, item in enumerate(lowerCamelCase__				):
						A:      Optional[Any]														=   item @ local_weight(lowerCamelCase__							,							lowerCamelCase__							,							lowerCamelCase__							,							lowerCamelCase__				)

			return y_pred



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      str							,							lowerCamelCase__   :      str							,							lowerCamelCase__   :      str				):
			import seaborn as sns

			A:      Optional[int]														=   sns.load_dataset(lowerCamelCase__				)
			A:      Any														=   np.array(data[x_name]				)
			A:      List[Any]														=   np.array(data[y_name]				)

			A:      List[str]														=   np.ones(len(lowerCamelCase__				)				)

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

			return x_train, x_data, y_data



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      np.ndarray							,							lowerCamelCase__   :      np.ndarray							,							lowerCamelCase__   :      np.ndarray							,							lowerCamelCase__   :      np.ndarray							,							lowerCamelCase__   :      str							,							lowerCamelCase__   :      str							,							):
			A:      str														=   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
					__SCREAMING_SNAKE_CASE :      Union[str, Any]             =load_data('tips', 'total_bill', 'tip')
					__SCREAMING_SNAKE_CASE :      Tuple             =local_weight_regression(training_data_x, tip, 5)
					plot_preds(training_data_x, predictions, total_bill, tip, 'total_bill', 'tip')










'''simple docstring'''
def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      list							,							lowerCamelCase__   :      int				):


			'''simple docstring'''







			for index, item in enumerate(lowerCamelCase__				):
						if item == target:
									return index
			return -1



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      list							,							lowerCamelCase__   :      int							,							lowerCamelCase__   :      int							,							lowerCamelCase__   :      int				):


			'''simple docstring'''







			if not (0 <= high < len(lowerCamelCase__				) and 0 <= low < len(lowerCamelCase__				)):
						raise Exception("""Invalid upper or lower bound!"""				)
			if high < low:
						return -1
			if sequence[low] == target:
						return low
			if sequence[high] == target:
						return high
			return rec_linear_search(lowerCamelCase__							,							low + 1							,							high - 1							,							lowerCamelCase__				)


if __name__ == "__main__":
					__SCREAMING_SNAKE_CASE :      List[str]             =input('Enter numbers separated by comma:\n').strip()
					__SCREAMING_SNAKE_CASE :      str             =[int(item.strip()) for item in user_input.split(',')]

					__SCREAMING_SNAKE_CASE :      List[str]             =int(input('Enter a single number to be found in the list:\n').strip())
					__SCREAMING_SNAKE_CASE :      List[Any]             =linear_search(sequence, target)
					if result != -1:
										print(f"""linear_search({sequence}, {target}) = {result}""")
					else:
										print(f"""{target} was not found in {sequence}""")





