


from typing import Literal

__snake_case     =		'''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				Union[str, Any]:


     """simple docstring"""


     __UpperCamelCase				=	LETTERS if mode == 'decrypt' else key
     __UpperCamelCase				=	key if mode == 'decrypt' else LETTERS
     __UpperCamelCase				=	''
     # loop through each symbol in the message
     for symbol in message:
          if symbol.upper() in chars_a:
               # encrypt/decrypt the symbol
               __UpperCamelCase				=	chars_a.find(symbol.upper()				)
               if symbol.isupper():
                    translated += chars_b[sym_index].upper()
               else:
                    translated += chars_b[sym_index].lower()
          else:
               # symbol is not in LETTERS, just add it
               translated += symbol
     return translated






def 						_A						(						_lowercase			,       _lowercase				)			->				List[str]:


     """simple docstring"""


     return translate_message(__UpperCamelCase			,       __UpperCamelCase			,       'encrypt'				)






def 						_A						(						_lowercase			,       _lowercase				)			->				Optional[Any]:


     """simple docstring"""


     return translate_message(__UpperCamelCase			,       __UpperCamelCase			,       'decrypt'				)






def 						_A						(						)			->				Optional[Any]:


     """simple docstring"""


     __UpperCamelCase				=	'Hello World'
     __UpperCamelCase				=	'QWERTYUIOPASDFGHJKLZXCVBNM'
     __UpperCamelCase				=	'decrypt'  # set to 'encrypt' or 'decrypt'

     if mode == "encrypt":
          __UpperCamelCase				=	encrypt_message(__UpperCamelCase			,       __UpperCamelCase				)
     elif mode == "decrypt":
          __UpperCamelCase				=	decrypt_message(__UpperCamelCase			,       __UpperCamelCase				)
     print(f'''Using the key {key}, the {mode}ed message is: {translated}'''				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()






import matplotlib.pyplot as plt
import numpy as np






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


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

     return weights






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


     __UpperCamelCase				=	weight_matrix(_lowercase			,       _lowercase			,       _lowercase				)
     __UpperCamelCase				=	np.linalg.inv(x_train.T @ weight_mat @ x_train				) @ (
         x_train.T @ weight_mat @ y_train.T
     )

     return weight






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				np.ndarray:


     """simple docstring"""


     __UpperCamelCase				=	np.zeros(len(_lowercase				)				)  # Initialize array of predictions
     for i, item in enumerate(_lowercase				):
          __UpperCamelCase				=	item @ local_weight(_lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)

     return y_pred






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase				)			->				tuple[np.ndarray, np.ndarray, np.ndarray]:


     """simple docstring"""


     import seaborn as sns

     __UpperCamelCase				=	sns.load_dataset(_lowercase				)
     __UpperCamelCase				=	np.array(data[x_name]				)
     __UpperCamelCase				=	np.array(data[y_name]				)

     __UpperCamelCase				=	np.ones(len(_lowercase				)				)

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

     return x_train, x_data, y_data






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       )			->				None:


     """simple docstring"""


     __UpperCamelCase				=	np.sort(_lowercase			,       axis=0				)
     plt.scatter(_lowercase			,       _lowercase			,       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(_lowercase				)
     plt.ylabel(_lowercase				)
     plt.show()


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # Demo with a dataset from the seaborn module
    __snake_case    ,					__snake_case    ,					__snake_case     =		load_data('''tips''', '''total_bill''', '''tip''')
    __snake_case     =		local_weight_regression(training_data_x, tip, 5)
    plot_preds(training_data_x, predictions, total_bill, tip, '''total_bill''', '''tip''')


