code: 
  
  
  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)
  
  
  
style_context: 
  
  
  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']				)
  
  
  