


'''simple docstring'''






import numpy as np




class 			a  :
 def __init__(			self   ,					__magic_name__=5   ,					__magic_name__=5    )       ->				Any:
      _a         =				depth
      _a         =				0
      _a         =				None
      _a         =				None
      _a         =				min_leaf_size
      _a         =				None
 def 				__UpperCAmelCase		(			self   ,					__magic_name__   ,					__magic_name__    )       ->				str:




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

      return np.mean((labels - prediction) ** 2    )
 def 				__UpperCAmelCase		(			self   ,					__magic_name__   ,					__magic_name__    )       ->				List[Any]:




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

      if self.depth == 1:
           _a         =				np.mean(__magic_name__    )
           return

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

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

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

      return




 def 				__UpperCAmelCase		(			self   ,					__magic_name__    )       ->				Any:




      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(__magic_name__    )
           else:
                return self.left.predict(__magic_name__    )
      else:
           print('Error: Decision tree not yet trained'    )
           return None





class 			a  :
 @staticmethod
 def 				__UpperCAmelCase		(			__magic_name__   ,					__magic_name__    )       ->				List[str]:
      _a         =				float(0    )
      for label in labels:
           squared_error_sum += (label - prediction) ** 2

      return float(squared_error_sum / labels.size    )






def 						_A					() ->	Optional[Any]:




     '''simple docstring'''



     _a         =				np.arange(-1.0					,    1.0					,    0.0_0_5		)
     _a         =				np.sin(A_		)

     _a         =				DecisionTree(depth=10					,    min_leaf_size=10		)
     tree.train(A_					,    A_		)

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

     print('Test values: ' + str(A_		)		)
     print('Predictions: ' + str(A_		)		)
     print('Average error: ' + str(A_		)		)


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

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









"""simple docstring"""


_UpperCamelCase							:       str       					=		10**8




def      snake_case    (A_ :int ):







  '''simple docstring'''

  if n % 1_0 == 0:
    return False
  a	:     Optional[int]							=  str(A_ )
  return s == s[::-1]




def      snake_case    ():







  '''simple docstring'''

  a	:     Tuple							=  set()
  a	:     Optional[Any]							=  1
  a	:     List[str]							=  5
  while sum_squares < LIMIT:
    a	:     int							=  first_square + 1
    while sum_squares < LIMIT:
      if is_palindrome(A_ ):
        answer.add(A_ )
      last_square += 1
      sum_squares += last_square**2
    first_square += 1
    a	:     List[str]							=  first_square**2 + (first_square + 1) ** 2

  return sum(A_ )


if __name__ == "__main__":
   print(solution())




