

'''simple docstring'''
import numpy as np


class      a__						:


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





      """simple docstring"""





      _lowercase    :   Any      =     depth
      _lowercase    :   Union[str, Any]      =     0
      _lowercase    :   Union[str, Any]      =     None
      _lowercase    :   List[Any]      =     None
      _lowercase    :   Optional[int]      =     min_leaf_size
      _lowercase    :   Optional[Any]      =     None


   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase    ,       _UpperCamelCase						):





      """simple docstring"""





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

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


   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase    ,       _UpperCamelCase						):





      """simple docstring"""





      if x.ndim != 1:
         print("Error: Input data set must be one dimensional"						)
         return
      if len(__a						) != len(__a						):
         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(__a						) < 2 * self.min_leaf_size:
         _lowercase    :   Any      =     np.mean(__a						)
         return

      if self.depth == 1:
         _lowercase    :   str      =     np.mean(__a						)
         return

      _lowercase    :   List[Any]      =     0
      _lowercase    :   Optional[Any]      =     self.mean_squared_error(__a    ,       np.mean(__a						)						) * 2
      for i in range(len(__a						)						):
         if len(x[:i]						) < self.min_leaf_size:
            continue
         elif len(x[i:]						) < self.min_leaf_size:
            continue
         else:
            _lowercase    :   List[str]      =     self.mean_squared_error(x[:i]    ,       np.mean(y[:i]						)						)
            _lowercase    :   List[Any]      =     self.mean_squared_error(x[i:]    ,       np.mean(y[i:]						)						)
            _lowercase    :   Any      =     error_left + error_right
            if error < min_error:
               _lowercase    :   List[Any]      =     i
               _lowercase    :   Optional[int]      =     error

      if best_split != 0:
         _lowercase    :   Dict      =     x[:best_split]
         _lowercase    :   str      =     y[:best_split]
         _lowercase    :   Any      =     x[best_split:]
         _lowercase    :   List[Any]      =     y[best_split:]

         _lowercase    :   str      =     x[best_split]
         _lowercase    :   Any      =     DecisionTree(
             depth=self.depth - 1    ,       min_leaf_size=self.min_leaf_size						)
         _lowercase    :   List[Any]      =     DecisionTree(
             depth=self.depth - 1    ,       min_leaf_size=self.min_leaf_size						)
         self.left.train(__a    ,       __a						)
         self.right.train(__a    ,       __a						)
      else:
         _lowercase    :   Union[str, Any]      =     np.mean(__a						)

      return






   def 		_lowerCamelCase     (				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(__a						)
         else:
            return self.left.predict(__a						)
      else:
         print("Error: Decision tree not yet trained"						)
         return None




class      a__						:


   @staticmethod
   def 		_lowerCamelCase     (				_UpperCamelCase    ,       _UpperCamelCase						):





      """simple docstring"""





      _lowercase    :   Tuple      =     float(0						)
      for label in labels:
         squared_error_sum += (label - prediction) ** 2

      return float(squared_error_sum / labels.size						)







def       _A	(			)    ->			Any:
   _lowercase    :   List[Any]      =     np.arange(-1.0	,							1.0	,							0.005  )
   _lowercase    :   Dict      =     np.sin(_SCREAMING_SNAKE_CASE  )

   _lowercase    :   Union[str, Any]      =     DecisionTree(depth=10	,							min_leaf_size=10  )
   tree.train(_SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE  )

   _lowercase    :   Any      =     (np.random.rand(10  ) * 2) - 1
   _lowercase    :   Any      =     np.array([tree.predict(_SCREAMING_SNAKE_CASE  ) for x in test_cases]  )
   _lowercase    :   Union[str, Any]      =     np.mean((predictions - test_cases) ** 2  )

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


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

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









'''simple docstring'''
def       _A	(			snake_case  )    ->			list:
   _lowercase    :   Optional[Any]      =     1.3
   _lowercase    :   Optional[Any]      =     len(snake_case  )
   _lowercase    :   Optional[int]      =     False

   while not completed:
      # Update the gap value for a next comb
      _lowercase    :   Tuple      =     int(gap / shrink_factor  )
      if gap <= 1:
         _lowercase    :   List[str]      =     True

      _lowercase    :   Optional[int]      =     0
      while index + gap < len(snake_case  ):
         if data[index] > data[index + gap]:
            # Swap values
            _lowercase  ,   _lowercase    :   Any      =     data[index + gap], data[index]
            _lowercase    :   Tuple      =     False
         index += 1

   return data


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    _snake_case     						=						input('Enter numbers separated by a comma:\n').strip()
    _snake_case     						=						[int(item) for item in user_input.split(',')]
    print(comb_sort(unsorted))






