code: 
  
  
  """simple docstring"""
  
  
  
  
  import numpy as np
  
  
  
  
  
  class __snake_case		:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     def __init__(		self							:Optional[Any]				,  UpperCamelCase__							:str=5				,  UpperCamelCase__							:int=5       ):
            _a          =				depth
            _a          =				0
            _a          =				None
            _a          =				None
            _a          =				min_leaf_size
            _a          =				None
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Any				,  UpperCamelCase__							:Any				,  UpperCamelCase__							:int       ):
            if labels.ndim != 1:
                   print("Error: Input labels must be one dimensional"       )
  
            return np.mean((labels - prediction) ** 2       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Any				,  UpperCamelCase__							:Optional[int]				,  UpperCamelCase__							:Dict       ):
            if x.ndim != 1:
                   print("Error: Input data set must be one dimensional"       )
                   return
            if len(UpperCamelCase__       ) != len(UpperCamelCase__       ):
                   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(UpperCamelCase__       ) < 2 * self.min_leaf_size:
                   _a          =				np.mean(UpperCamelCase__       )
                   return
  
            if self.depth == 1:
                   _a          =				np.mean(UpperCamelCase__       )
                   return
  
            _a          =				0
            _a          =				self.mean_squared_error(UpperCamelCase__				,  np.mean(UpperCamelCase__       )       ) * 2
            for i in range(len(UpperCamelCase__       )       ):
                   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(UpperCamelCase__				,  UpperCamelCase__       )
                   self.right.train(UpperCamelCase__				,  UpperCamelCase__       )
            else:
                   _a          =				np.mean(UpperCamelCase__       )
  
            return
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Optional[int]				,  UpperCamelCase__							:Optional[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(UpperCamelCase__       )
                   else:
                          return self.left.predict(UpperCamelCase__       )
            else:
                   print("Error: Decision tree not yet trained"       )
                   return None
  
  
  
  
  
  
  class __snake_case		:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     @staticmethod
     def SCREAMING_SNAKE_CASE_      (		UpperCamelCase__							:Any				,  UpperCamelCase__							:Tuple       ):
            _a          =				float(0       )
            for label in labels:
                   squared_error_sum += (label - prediction) ** 2
  
            return float(squared_error_sum / labels.size       )
  
  
  
  
  
  
  def 						__a      (  ):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				np.arange(-1.0,		1.0,		0.005					)
         _a          =				np.sin(a					)
  
         _a          =				DecisionTree(depth=1_0,		min_leaf_size=1_0					)
         tree.train(a,		a					)
  
         _a          =				(np.random.rand(1_0					) * 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)
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  from __future__ import annotations
  
  
  
  def 						__a      (  a,		a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         assert len(a					) == len(a					)
  
         _a          =				len(a					)
         _a          =				list(range(a					)					)
         _a          =				[-1] * n  # who the donor has donated to
         _a          =				[-1] * n  # who the recipient has received from
         _a          =				[0] * n
  
         while unmatched_donors:
                _a          =				unmatched_donors[0]
                _a          =				donor_pref[donor]
                _a          =				donor_preference[num_donations[donor]]
                num_donations[donor] += 1
                _a          =				recipient_pref[recipient]
                _a          =				rec_record[recipient]
  
                if prev_donor != -1:
                       if rec_preference.index(a					) > rec_preference.index(a					):
                              _a          =				donor
                              _a          =				recipient
                              unmatched_donors.append(a					)
                              unmatched_donors.remove(a					)
                else:
                       _a          =				donor
                       _a          =				recipient
                       unmatched_donors.remove(a					)
         return donor_record
  