


'''simple docstring'''

from __future__ import annotations





def __snake_case (				UpperCAmelCase_						:						int		):
 for r in range(1 ,    UpperCAmelCase_		):
  lowerCamelCase_								=  []
  for x in range(n - 1		):
   lowerCamelCase_								=  pow(UpperCAmelCase_ ,    UpperCAmelCase_ ,    UpperCAmelCase_		)
   if val in li:
    break
   li.append(UpperCAmelCase_		)
  else:
   return r
 return None


if __name__ == "__main__":
     a_  :    Any      =			int(input("""Enter a prime number q: """))
     a_  :    Any      =			find_primitive(q)
     if a is None:
          print(f'''Cannot find the primitive for the value: {a!r}''')
     else:
          a_  :    List[str]      =			int(input("""Enter private key of A: """))
          a_  :    int      =			pow(a, a_private, q)
          a_  :    Tuple      =			int(input("""Enter private key of B: """))
          a_  :    Union[str, Any]      =			pow(a, b_private, q)

          a_  :    Union[str, Any]      =			pow(b_public, a_private, q)
          a_  :    Dict      =			pow(a_public, b_private, q)

          print("""The key value generated by A is: """, a_secret)
          print("""The key value generated by B is: """, b_secret)







'''simple docstring'''

import numpy as np





class 					snake_case   :





  """simple docstring"""




  def __init__(   self      ,  UpperCamelCase=5      ,  UpperCamelCase=5					):







   """simple docstring"""
   lowerCamelCase_								=  depth
   lowerCamelCase_								=  0
   lowerCamelCase_								=  None
   lowerCamelCase_								=  None
   lowerCamelCase_								=  min_leaf_size
   lowerCamelCase_								=  None






  def   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase					):







   """simple docstring"""
   if labels.ndim != 1:
    print("Error: Input labels must be one dimensional"					)

   return np.mean((labels - prediction) ** 2					)






  def   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase					):







   """simple docstring"""
   if x.ndim != 1:
    print("Error: Input data set must be one dimensional"					)
    return
   if len(UpperCamelCase					) != len(UpperCamelCase					):
    print("Error: X and y have different lengths"					)
    return
   if y.ndim != 1:
    print("Error: Data set labels must be one dimensional"					)
    return

   if len(UpperCamelCase					) < 2 * self.min_leaf_size:
    lowerCamelCase_								=  np.mean(UpperCamelCase					)
    return

   if self.depth == 1:
    lowerCamelCase_								=  np.mean(UpperCamelCase					)
    return

   lowerCamelCase_								=  0
   lowerCamelCase_								=  self.mean_squared_error(UpperCamelCase      ,  np.mean(UpperCamelCase					)					) * 2
   for i in range(len(UpperCamelCase					)					):
    if len(x[:i]					) < self.min_leaf_size:
     continue
    elif len(x[i:]					) < self.min_leaf_size:
     continue
    else:
     lowerCamelCase_								=  self.mean_squared_error(x[:i]      ,  np.mean(y[:i]					)					)
     lowerCamelCase_								=  self.mean_squared_error(x[i:]      ,  np.mean(y[i:]					)					)
     lowerCamelCase_								=  error_left + error_right
     if error < min_error:
      lowerCamelCase_								=  i
      lowerCamelCase_								=  error

   if best_split != 0:
    lowerCamelCase_								=  x[:best_split]
    lowerCamelCase_								=  y[:best_split]
    lowerCamelCase_								=  x[best_split:]
    lowerCamelCase_								=  y[best_split:]

    lowerCamelCase_								=  x[best_split]
    lowerCamelCase_								=  DecisionTree(
        depth=self.depth - 1      ,  min_leaf_size=self.min_leaf_size					)
    lowerCamelCase_								=  DecisionTree(
        depth=self.depth - 1      ,  min_leaf_size=self.min_leaf_size					)
    self.left.train(UpperCamelCase      ,  UpperCamelCase					)
    self.right.train(UpperCamelCase      ,  UpperCamelCase					)
   else:
    lowerCamelCase_								=  np.mean(UpperCamelCase					)

   return







  def   snake_case		(   self      ,  UpperCamelCase					):







   """simple docstring"""
   if self.prediction is not None:
    return self.prediction
   elif self.left or self.right is not None:
    if x >= self.decision_boundary:
     return self.right.predict(UpperCamelCase					)
    else:
     return self.left.predict(UpperCamelCase					)
   else:
    print("Error: Decision tree not yet trained"					)
    return None





class 					snake_case   :





  """simple docstring"""




  @staticmethod
  def   snake_case		(   UpperCamelCase      ,  UpperCamelCase					):







   """simple docstring"""
   lowerCamelCase_								=  float(0					)
   for label in labels:
    squared_error_sum += (label - prediction) ** 2

   return float(squared_error_sum / labels.size					)





def __snake_case (				):
 lowerCamelCase_								=  np.arange(-1.0 ,    1.0 ,    0.005		)
 lowerCamelCase_								=  np.sin(UpperCAmelCase_		)

 lowerCamelCase_								=  DecisionTree(depth=10 ,    min_leaf_size=10		)
 tree.train(UpperCAmelCase_ ,    UpperCAmelCase_		)

 lowerCamelCase_								=  (np.random.rand(10		) * 2) - 1
 lowerCamelCase_								=  np.array([tree.predict(UpperCAmelCase_		) for x in test_cases]		)
 lowerCamelCase_								=  np.mean((predictions - test_cases) ** 2		)

 print("Test values: " + str(UpperCAmelCase_		)		)
 print("Predictions: " + str(UpperCAmelCase_		)		)
 print("Average error: " + str(UpperCAmelCase_		)		)


if __name__ == "__main__":
     main()
     import doctest

     doctest.testmod(name="""mean_squarred_error""", verbose=True)



