




import numpy as np






class 			A			:
     """simple docstring"""

     def __init__(    self  :   int	,  A__  :   int=5	,  A__  :   int=5			)				->       int:
           lowerCamelCase__        =				depth
           lowerCamelCase__        =				0
           lowerCamelCase__        =				None
           lowerCamelCase__        =				None
           lowerCamelCase__        =				min_leaf_size
           lowerCamelCase__        =				None




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

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




     def  __snake_case   (    self  :   List[str]	,  A__  :   Dict	,  A__  :   Any			)				->       Dict:
           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:
                 lowerCamelCase__        =				np.mean(A__			)
                 return

           if self.depth == 1:
                 lowerCamelCase__        =				np.mean(A__			)
                 return

           lowerCamelCase__        =				0
           lowerCamelCase__        =				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:
                       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(A__	,  A__			)
                 self.right.train(A__	,  A__			)
           else:
                 lowerCamelCase__        =				np.mean(A__			)

           return




     def  __snake_case   (    self  :   List[str]	,  A__  :   Tuple			)				->       str:
           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			:
     """simple docstring"""

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

           return float(squared_error_sum / labels.size			)

def 			lowerCAmelCase_			(  )					->			List[str]:


      '''simple docstring'''




      lowerCamelCase__        =				np.arange(-1.0     ,				1.0     ,				0.005						)
      lowerCamelCase__        =				np.sin(lowerCamelCase						)

      lowerCamelCase__        =				DecisionTree(depth=10     ,				min_leaf_size=10						)
      tree.train(lowerCamelCase     ,				lowerCamelCase						)

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

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


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

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





from ..utils import DummyObject, requires_backends






class 			A			(		metaclass=_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:	Optional[Any]     =["torch", "transformers", "invisible_watermark"]




     def __init__(    self  :   Dict	,  *A__  :   Optional[int]	,  **A__  :   Dict			)				->       Union[str, Any]:
           requires_backends(self	,  ['torch', 'transformers', 'invisible_watermark']			)




     @classmethod
     def  __snake_case   (    cls  :   Optional[int]	,  *A__  :   Dict	,  **A__  :   List[str]			)				->       Union[str, Any]:
           requires_backends(cls	,  ['torch', 'transformers', 'invisible_watermark']			)




     @classmethod
     def  __snake_case   (    cls  :   Optional[Any]	,  *A__  :   Dict	,  **A__  :   List[Any]			)				->       Dict:
           requires_backends(cls	,  ['torch', 'transformers', 'invisible_watermark']			)






class 			A			(		metaclass=_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:	Dict     =["torch", "transformers", "invisible_watermark"]




     def __init__(    self  :   List[str]	,  *A__  :   str	,  **A__  :   int			)				->       List[Any]:
           requires_backends(self	,  ['torch', 'transformers', 'invisible_watermark']			)




     @classmethod
     def  __snake_case   (    cls  :   str	,  *A__  :   Any	,  **A__  :   Dict			)				->       Optional[Any]:
           requires_backends(cls	,  ['torch', 'transformers', 'invisible_watermark']			)




     @classmethod
     def  __snake_case   (    cls  :   List[str]	,  *A__  :   Optional[Any]	,  **A__  :   int			)				->       Dict:
           requires_backends(cls	,  ['torch', 'transformers', 'invisible_watermark']			)