import numpy as np




class 				__lowerCAmelCase					:



 '''simple docstring'''

 def __init__(     self    :						Dict    ,     lowerCamelCase    :						List[str]=5    ,     lowerCamelCase    :						Tuple=5					)	->							int:
        _UpperCamelCase       :    Any					  =					depth
        _UpperCamelCase       :    str					  =					0
        _UpperCamelCase       :    str					  =					None
        _UpperCamelCase       :    str					  =					None
        _UpperCamelCase       :    Dict					  =					min_leaf_size
        _UpperCamelCase       :    Union[str, Any]					  =					None

 def    __snake_case   (     self    :						Optional[Any]    ,     lowerCamelCase    :						int    ,     lowerCamelCase    :						int					)	->							Dict:
        if labels.ndim != 1:
               print('Error: Input labels must be one dimensional'					)

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

 def    __snake_case   (     self    :						Any    ,     lowerCamelCase    :						Any    ,     lowerCamelCase    :						List[str]					)	->							Optional[Any]:
        if x.ndim != 1:
               print('Error: Input data set must be one dimensional'					)
               return
        if len(lowerCamelCase					) != len(lowerCamelCase					):
               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(lowerCamelCase					) < 2 * self.min_leaf_size:
               _UpperCamelCase       :    str					  =					np.mean(lowerCamelCase					)
               return

        if self.depth == 1:
               _UpperCamelCase       :    List[str]					  =					np.mean(lowerCamelCase					)
               return

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

        if best_split != 0:
               _UpperCamelCase       :    int					  =					x[:best_split]
               _UpperCamelCase       :    Any					  =					y[:best_split]
               _UpperCamelCase       :    Union[str, Any]					  =					x[best_split:]
               _UpperCamelCase       :    str					  =					y[best_split:]

               _UpperCamelCase       :    Tuple					  =					x[best_split]
               _UpperCamelCase       :    str					  =					DecisionTree(
                   depth=self.depth - 1    ,     min_leaf_size=self.min_leaf_size					)
               _UpperCamelCase       :    Optional[Any]					  =					DecisionTree(
                   depth=self.depth - 1    ,     min_leaf_size=self.min_leaf_size					)
               self.left.train(lowerCamelCase    ,     lowerCamelCase					)
               self.right.train(lowerCamelCase    ,     lowerCamelCase					)
        else:
               _UpperCamelCase       :    Dict					  =					np.mean(lowerCamelCase					)

        return

 def    __snake_case   (     self    :						List[str]    ,     lowerCamelCase    :						List[Any]					)	->							Dict:
        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(lowerCamelCase					)
               else:
                      return self.left.predict(lowerCamelCase					)
        else:
               print('Error: Decision tree not yet trained'					)
               return None







class 				__lowerCAmelCase					:



 '''simple docstring'''

 @staticmethod
 def    __snake_case   (     lowerCamelCase    :						Optional[Any]    ,     lowerCamelCase    :						Any					)	->							List[Any]:
        _UpperCamelCase       :    List[str]					  =					float(0					)
        for label in labels:
               squared_error_sum += (label - prediction) ** 2

        return float(squared_error_sum / labels.size					)







def SCREAMING_SNAKE_CASE			(       ):
       _UpperCamelCase       :    Optional[Any]					  =					np.arange(-1.0						,  1.0						,  0.005			)
       _UpperCamelCase       :    Optional[Any]					  =					np.sin(lowercase_			)

       _UpperCamelCase       :    str					  =					DecisionTree(depth=10						,  min_leaf_size=10			)
       tree.train(lowercase_						,  lowercase_			)

       _UpperCamelCase       :    Tuple					  =					(np.random.rand(10			) * 2) - 1
       _UpperCamelCase       :    Optional[Any]					  =					np.array([tree.predict(lowercase_			) for x in test_cases]			)
       _UpperCamelCase       :    Tuple					  =					np.mean((predictions - test_cases) ** 2			)

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


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

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




def SCREAMING_SNAKE_CASE			(       lowercase_:		int = 4000000			):
       _UpperCamelCase       :    str					  =					1
       _UpperCamelCase       :    str					  =					2
       _UpperCamelCase       :    Tuple					  =					0
       while j <= n:
              if j % 2 == 0:
                     total += j
              _UpperCamelCase  , _UpperCamelCase       :    int					  =					j, i + j

       return total


if __name__ == "__main__":
 print(f'''{solution() = }''')



