




import matplotlib.pyplot as plt
import numpy as np
def 							__lowerCAmelCase			(UpperCAmelCase_ ,     UpperCAmelCase_ ,     UpperCAmelCase_		):
    a__  			=							len(UpperCamelCase__		)  # Number of training samples
    a__  			=							np.eye(UpperCamelCase__		)  # Initialize weights as identity matrix
    for j in range(UpperCamelCase__		):
        a__  			=							point - x_train[j]
        a__  			=							np.exp(diff @ diff.T / (-2.0 * tau**2)		)

    return weights
def 							__lowerCAmelCase			(UpperCAmelCase_ ,     UpperCAmelCase_ ,     UpperCAmelCase_ ,     UpperCAmelCase_		):
    a__  			=							weight_matrix(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__		)
    a__  			=							np.linalg.inv(x_train.T @ weight_mat @ x_train		) @ (
        x_train.T @ weight_mat @ y_train.T
    )

    return weight
def 							__lowerCAmelCase			(UpperCAmelCase_ ,     UpperCAmelCase_ ,     UpperCAmelCase_		):
    a__  			=							np.zeros(len(UpperCamelCase__		)		)  # Initialize array of predictions
    for i, item in enumerate(UpperCamelCase__		):
        a__  			=							item @ local_weight(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__		)

    return y_pred
def 							__lowerCAmelCase			(UpperCAmelCase_ ,     UpperCAmelCase_ ,     UpperCAmelCase_		):
    import seaborn as sns

    a__  			=							sns.load_dataset(UpperCamelCase__		)
    a__  			=							np.array(data[x_name]		)
    a__  			=							np.array(data[y_name]		)

    a__  			=							np.ones(len(UpperCamelCase__		)		)

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

    return x_train, x_data, y_data
def 							__lowerCAmelCase			(UpperCAmelCase_ ,     UpperCAmelCase_ ,     UpperCAmelCase_ ,     UpperCAmelCase_ ,     UpperCAmelCase_ ,     UpperCAmelCase_ ,     ):
    a__  			=							np.sort(UpperCamelCase__ ,     axis=0		)
    plt.scatter(UpperCamelCase__ ,     UpperCamelCase__ ,     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(UpperCamelCase__		)
    plt.ylabel(UpperCamelCase__		)
    plt.show()


if __name__ == "__main__":
 import doctest

 doctest.testmod()

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












def      UpperCAmelCase  (						UpperCamelCase__   )     ->     int:







				'''simple docstring'''


				barcode //= 10  # exclude the last digit
				__lowerCAmelCase					=  False
				__lowerCAmelCase					=  0

				# extract and check each digit
				while barcode != 0:
								__lowerCAmelCase					=  1 if checker else 3
								s += mult * (barcode % 10)
								barcode //= 10
								__lowerCAmelCase					=  not checker

				return (10 - (s % 10)) % 10





def      UpperCAmelCase  (						UpperCamelCase__   )     ->     bool:







				'''simple docstring'''


				return len(str(UpperCamelCase__   )   ) == 13 and get_check_digit(UpperCamelCase__   ) == barcode % 10





def      UpperCAmelCase  (						UpperCamelCase__   )     ->     int:







				'''simple docstring'''


				if str(UpperCamelCase__   ).isalpha():
								__lowerCAmelCase					=  F'''Barcode \'{barcode}\' has alphabetic characters.'''
								raise ValueError(UpperCamelCase__   )
				elif int(UpperCamelCase__   ) < 0:
								raise ValueError("""The entered barcode has a negative value. Try again."""   )
				else:
								return int(UpperCamelCase__   )


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	__A						:		Tuple          =						get_barcode(input("Barcode: ").strip())

	if is_valid(barcode):
		print(f"""'{barcode}' is a valid barcode.""")
	else:
		print(f"""'{barcode}' is NOT a valid barcode.""")





