


import numpy as np



class   __lowerCamelCase :







       def __init__( self:   Tuple,A_:   Any=5,A_:   Dict=5				):






            '''simple docstring'''




            __UpperCamelCase   = depth
            __UpperCamelCase   = 0
            __UpperCamelCase   = None
            __UpperCamelCase   = None
            __UpperCamelCase   = min_leaf_size
            __UpperCamelCase   = None







       def 		snake_case_  ( self:   Optional[Any],A_:   int,A_:   Dict				):






            '''simple docstring'''




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

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







       def 		snake_case_  ( self:   str,A_:   str,A_:   Union[str, Any]				):






            '''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:
                 __UpperCamelCase   = np.mean(A_				)
                 return

            if self.depth == 1:
                 __UpperCamelCase   = np.mean(A_				)
                 return

            __UpperCamelCase   = 0
            __UpperCamelCase   = 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:
                      __UpperCamelCase   = self.mean_squared_error(x[:i],np.mean(y[:i]				)				)
                      __UpperCamelCase   = self.mean_squared_error(x[i:],np.mean(y[i:]				)				)
                      __UpperCamelCase   = error_left + error_right
                      if error < min_error:
                           __UpperCamelCase   = i
                           __UpperCamelCase   = error

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

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

            return







       def 		snake_case_  ( self:   Tuple,A_:   Union[str, Any]				):






            '''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   __lowerCamelCase :







       @staticmethod
       def 		snake_case_  ( A_:   str,A_:   List[str]				):






            '''simple docstring'''




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

            return float(squared_error_sum / labels.size				)






def 		_A		(		)				->   Any:


     """simple docstring"""


     __UpperCamelCase   = np.arange(-1.0				,     1.0				,     0.0_05   )
     __UpperCamelCase   = np.sin(_lowercase   )

     __UpperCamelCase   = DecisionTree(depth=10				,     min_leaf_size=10   )
     tree.train(_lowercase				,     _lowercase   )

     __UpperCamelCase   = (np.random.rand(10   ) * 2) - 1
     __UpperCamelCase   = np.array([tree.predict(_lowercase   ) for x in test_cases]   )
     __UpperCamelCase   = 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)






from ..utils import DummyObject, requires_backends



class   __lowerCamelCase (metaclass=_a	):
       _lowercase       							=    ["""torch""", """transformers""", """k_diffusion"""]







       def __init__( self:   Tuple,*A_:   Union[str, Any],**A_:   Tuple				):






            '''simple docstring'''




            requires_backends(self,['torch', 'transformers', 'k_diffusion']				)







       @classmethod
       def 		snake_case_  ( cls:   int,*A_:   Optional[Any],**A_:   Tuple				):






            '''simple docstring'''




            requires_backends(cls,['torch', 'transformers', 'k_diffusion']				)







       @classmethod
       def 		snake_case_  ( cls:   int,*A_:   Tuple,**A_:   Tuple				):






            '''simple docstring'''




            requires_backends(cls,['torch', 'transformers', 'k_diffusion']				)


