code: 
  """simple docstring"""
  
  
  
  
  
  import matplotlib.pyplot as plt
  import numpy as np
  
  
  
  
  
  
  
  def 			__a  (	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase						):
     UpperCAmelCase_      :      List[str]      =					len(__lowerCamelCase						)  # Number of training samples
     UpperCAmelCase_      :      Optional[int]      =					np.eye(__lowerCamelCase						)  # Initialize weights as identity matrix
     for j in range(__lowerCamelCase						):
        UpperCAmelCase_      :      Optional[int]      =					point - x_train[j]
        UpperCAmelCase_      :      Any      =					np.exp(diff @ diff.T / (-2.0 * tau**2)						)
  
     return weights
  
  
  
  
  
  
  
  def 			__a  (	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase						):
     UpperCAmelCase_      :      Optional[Any]      =					weight_matrix(__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase						)
     UpperCAmelCase_      :      Optional[int]      =					np.linalg.inv(x_train.T @ weight_mat @ x_train						) @ (
         x_train.T @ weight_mat @ y_train.T
     )
  
     return weight
  
  
  
  
  
  
  
  def 			__a  (	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase						):
     UpperCAmelCase_      :      Optional[Any]      =					np.zeros(len(__lowerCamelCase						)						)  # Initialize array of predictions
     for i, item in enumerate(__lowerCamelCase						):
        UpperCAmelCase_      :      Dict      =					item @ local_weight(__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase						)
  
     return y_pred
  
  
  
  
  
  
  
  def 			__a  (	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase						):
     import seaborn as sns
  
     UpperCAmelCase_      :      List[str]      =					sns.load_dataset(__lowerCamelCase						)
     UpperCAmelCase_      :      List[Any]      =					np.array(data[x_name]						)
     UpperCAmelCase_      :      Union[str, Any]      =					np.array(data[y_name]						)
  
     UpperCAmelCase_      :      Optional[Any]      =					np.ones(len(__lowerCamelCase						)						)
  
     # pairing elements of one and x_data
     UpperCAmelCase_      :      Union[str, Any]      =					np.column_stack((one, x_data)						)
  
     return x_train, x_data, y_data
  
  
  
  
  
  
  
  def 			__a  (	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase,	):
     UpperCAmelCase_      :      Union[str, Any]      =					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
      _a , _a , _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')
  
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  from string import ascii_uppercase
  
  
  
  
  
  
  
  def 			__a  (	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase = False,	__lowerCamelCase = ascii_uppercase						):
     UpperCAmelCase_      :      List[str]      =					keyword.upper()
     UpperCAmelCase_      :      str      =					plaintext.upper()
     UpperCAmelCase_      :      Tuple      =					set(__lowerCamelCase						)
  
     # create a list of unique characters in the keyword - their order matters
     # it determines how we will map plaintext characters to the ciphertext
     UpperCAmelCase_      :      Tuple      =					[]
     for char in keyword:
        if char in alphabet_set and char not in unique_chars:
           unique_chars.append(__lowerCamelCase						)
      # the number of those unique characters will determine the number of rows
     UpperCAmelCase_      :      Union[str, Any]      =					len(__lowerCamelCase						)
  
     # create a shifted version of the alphabet
     UpperCAmelCase_      :      Tuple      =					unique_chars + [
         char for char in alphabet if char not in unique_chars
     ]
  
     # create a modified alphabet by splitting the shifted alphabet into rows
     UpperCAmelCase_      :      List[str]      =					[
         shifted_alphabet[k : k + num_unique_chars_in_keyword]
         for k in range(0,	26,	__lowerCamelCase						)
     ]
  
     # map the alphabet characters to the modified alphabet characters
     # going 'vertically' through the modified alphabet - consider columns first
     UpperCAmelCase_      :      Tuple      =					{}
     UpperCAmelCase_      :      Dict      =					0
     for column in range(__lowerCamelCase						):
        for row in modified_alphabet:
           # if current row (the last one) is too short, break out of loop
           if len(__lowerCamelCase						) <= column:
              break
  
           # map current letter to letter in modified alphabet
           UpperCAmelCase_      :      Tuple      =					row[column]
           letter_index += 1
  
     if verbose:
        print(__lowerCamelCase						)
     # create the encrypted text by mapping the plaintext to the modified alphabet
     return "".join(mapping[char] if char in mapping else char for char in plaintext						)
  
  
  if __name__ == "__main__":
      # example use
      print(mixed_keyword('college', 'UNIVERSITY'))
  
  
  