




'''simple docstring'''
import numpy as np
class      __UpperCAmelCase							:



     '''simple docstring'''





     def __init__(  self    :	Optional[Any]			,					_lowercase    :	str=5			,					_lowercase    :	int=5)						->       Optional[int]:
        A_       		=   depth
        A_       		=   0
        A_       		=   None
        A_       		=   None
        A_       		=   min_leaf_size
        A_       		=   None



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

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



     def 						__snake_case	(  self    :	Any			,					_lowercase    :	Optional[int]			,					_lowercase    :	Dict)						->       Union[str, Any]:
        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 						__snake_case	(  self    :	Optional[int]			,					_lowercase    :	Optional[Any])						->       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      __UpperCAmelCase							:



     '''simple docstring'''





     @staticmethod
     def 						__snake_case	(  _lowercase    :	Any			,					_lowercase    :	Tuple)						->       str:
        A_       		=   float(0)
        for label in labels:
           squared_error_sum += (label - prediction) ** 2

        return float(squared_error_sum / labels.size)





def  lowerCamelCase(       )		->		Union[str, Any]:
   A_       		=   np.arange(-1.0					,1.0					,0.005    )
   A_       		=   np.sin(SCREAMING_SNAKE_CASE_    )

   A_       		=   DecisionTree(depth=10					,min_leaf_size=10    )
   tree.train(SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_    )

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

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


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

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










'''simple docstring'''
def  lowerCamelCase(       SCREAMING_SNAKE_CASE_    )		->		int:

			if not isinstance(SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_    ) or number < 0:
						raise ValueError('Input must be a non-negative integer'    )

			A_       		=   number & ~(number - 1)
			A_       		=   0
			while intermediate:
						intermediate >>= 1
						index += 1
			return index - 1


if __name__ == "__main__":
							import doctest

							doctest.testmod(verbose=True)




