
import numpy as np







class  _lowerCamelCase			:

     def __init__(  self							,       SCREAMING_SNAKE_CASE_=5							,       SCREAMING_SNAKE_CASE_=5    ):
            __snake_case						       =   depth
            __snake_case						       =   0
            __snake_case						       =   None
            __snake_case						       =   None
            __snake_case						       =   min_leaf_size
            __snake_case						       =   None

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
            if labels.ndim != 1:
                   print('Error: Input labels must be one dimensional'    )

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

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
            if x.ndim != 1:
                   print('Error: Input data set must be one dimensional'    )
                   return
            if len(SCREAMING_SNAKE_CASE_    ) != len(SCREAMING_SNAKE_CASE_    ):
                   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(SCREAMING_SNAKE_CASE_    ) < 2 * self.min_leaf_size:
                   __snake_case						       =   np.mean(SCREAMING_SNAKE_CASE_    )
                   return

            if self.depth == 1:
                   __snake_case						       =   np.mean(SCREAMING_SNAKE_CASE_    )
                   return

            __snake_case						       =   0
            __snake_case						       =   self.mean_squared_error(SCREAMING_SNAKE_CASE_							,       np.mean(SCREAMING_SNAKE_CASE_    )    ) * 2
            for i in range(len(SCREAMING_SNAKE_CASE_    )    ):
                   if len(x[:i]    ) < self.min_leaf_size:
                          continue
                   elif len(x[i:]    ) < self.min_leaf_size:
                          continue
                   else:
                          __snake_case						       =   self.mean_squared_error(x[:i]							,       np.mean(y[:i]    )    )
                          __snake_case						       =   self.mean_squared_error(x[i:]							,       np.mean(y[i:]    )    )
                          __snake_case						       =   error_left + error_right
                          if error < min_error:
                                 __snake_case						       =   i
                                 __snake_case						       =   error

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

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

            return

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_    ):
            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(SCREAMING_SNAKE_CASE_    )
                   else:
                          return self.left.predict(SCREAMING_SNAKE_CASE_    )
            else:
                   print('Error: Decision tree not yet trained'    )
                   return None







class  _lowerCamelCase			:

     @staticmethod
     def 						__lowerCamelCase				(  SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   float(0    )
            for label in labels:
                   squared_error_sum += (label - prediction) ** 2

            return float(squared_error_sum / labels.size    )





def        __lowercase(							)       ->   str:
       __snake_case						       =   np.arange(-1.0    ,1.0    ,0.0_05					)
       __snake_case						       =   np.sin(__snake_case					)

       __snake_case						       =   DecisionTree(depth=10    ,min_leaf_size=10					)
       tree.train(__snake_case    ,__snake_case					)

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

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


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

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





'''simple docstring'''



import math



def 	__lowerCAmelCase       (				lowerCamelCase :    int       ):






	'''simple docstring'''




	if not isinstance(lowerCamelCase					,		lowerCamelCase       ):
		__lowerCAmelCase       				=	f'''Input value of [number={number}] must be an integer'''
		raise TypeError(lowerCamelCase       )
	if number < 1:
		__lowerCAmelCase       				=	f'''Input value of [number={number}] must be a positive integer'''
		raise ValueError(lowerCamelCase       )
	__lowerCAmelCase       				=	[number]
	while number != 1:
		if number % 2 == 0:
			__lowerCAmelCase       				=	math.floor(math.sqrt(lowerCamelCase       )       )
		else:
			__lowerCAmelCase       				=	math.floor(
			    math.sqrt(lowerCamelCase       ) * math.sqrt(lowerCamelCase       ) * math.sqrt(lowerCamelCase       )       )
		sequence.append(lowerCamelCase       )
	return sequence


if __name__ == "__main__":
	import doctest

	doctest.testmod()