






"""simple docstring"""



import numpy as np




class  lowerCAmelCase__ :
      def __init__(		self			:  List[str]       , snake_case__			:  Tuple=5       , snake_case__			:  Union[str, Any]=5       ):


           '''simple docstring'''
           UpperCAmelCase__	:						int       =       depth
           UpperCAmelCase__	:						Any       =       0
           UpperCAmelCase__	:						Tuple       =       None
           UpperCAmelCase__	:						List[str]       =       None
           UpperCAmelCase__	:						Optional[int]       =       min_leaf_size
           UpperCAmelCase__	:						Optional[int]       =       None
      def 			__a       (		self			:  Optional[int]       , snake_case__			:  int       , snake_case__			:  Any       ):


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

           return np.mean((labels - prediction) ** 2       )
      def 			__a       (		self			:  str       , snake_case__			:  Union[str, Any]       , snake_case__			:  List[Any]       ):


           '''simple docstring'''
           if x.ndim != 1:
                print("Error: Input data set must be one dimensional"       )
                return
           if len(snake_case__       ) != len(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(snake_case__       ) < 2 * self.min_leaf_size:
                UpperCAmelCase__	:						Union[str, Any]       =       np.mean(snake_case__       )
                return

           if self.depth == 1:
                UpperCAmelCase__	:						Optional[int]       =       np.mean(snake_case__       )
                return

           UpperCAmelCase__	:						str       =       0
           UpperCAmelCase__	:						Optional[int]       =       self.mean_squared_error(snake_case__       , np.mean(snake_case__       )       ) * 2
           for i in range(len(snake_case__       )       ):
                if len(x[:i]       ) < self.min_leaf_size:
                     continue
                elif len(x[i:]       ) < self.min_leaf_size:
                     continue
                else:
                     UpperCAmelCase__	:						Optional[Any]       =       self.mean_squared_error(x[:i]       , np.mean(y[:i]       )       )
                     UpperCAmelCase__	:						int       =       self.mean_squared_error(x[i:]       , np.mean(y[i:]       )       )
                     UpperCAmelCase__	:						Union[str, Any]       =       error_left + error_right
                     if error < min_error:
                          UpperCAmelCase__	:						Dict       =       i
                          UpperCAmelCase__	:						List[str]       =       error

           if best_split != 0:
                UpperCAmelCase__	:						Optional[Any]       =       x[:best_split]
                UpperCAmelCase__	:						str       =       y[:best_split]
                UpperCAmelCase__	:						Any       =       x[best_split:]
                UpperCAmelCase__	:						Any       =       y[best_split:]

                UpperCAmelCase__	:						Any       =       x[best_split]
                UpperCAmelCase__	:						List[Any]       =       DecisionTree(
                    depth=self.depth - 1       , min_leaf_size=self.min_leaf_size       )
                UpperCAmelCase__	:						Any       =       DecisionTree(
                    depth=self.depth - 1       , min_leaf_size=self.min_leaf_size       )
                self.left.train(snake_case__       , snake_case__       )
                self.right.train(snake_case__       , snake_case__       )
           else:
                UpperCAmelCase__	:						List[str]       =       np.mean(snake_case__       )

           return




      def 			__a       (		self			:  List[Any]       , snake_case__			:  Optional[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(snake_case__       )
                else:
                     return self.left.predict(snake_case__       )
           else:
                print("Error: Decision tree not yet trained"       )
                return None




class  lowerCAmelCase__ :
      @staticmethod
      def 			__a       (		snake_case__			:  Any       , snake_case__			:  int       ):


           '''simple docstring'''
           UpperCAmelCase__	:						List[Any]       =       float(0       )
           for label in labels:
                squared_error_sum += (label - prediction) ** 2

           return float(squared_error_sum / labels.size       )




def   SCREAMING_SNAKE_CASE__					(     )->		Dict:






     '''simple docstring'''



     UpperCAmelCase__	:						str       =       np.arange(-1.0 ,				1.0 ,				0.005							)
     UpperCAmelCase__	:						str       =       np.sin(lowercase__							)

     UpperCAmelCase__	:						Union[str, Any]       =       DecisionTree(depth=10 ,				min_leaf_size=10							)
     tree.train(lowercase__ ,				lowercase__							)

     UpperCAmelCase__	:						Union[str, Any]       =       (np.random.rand(10							) * 2) - 1
     UpperCAmelCase__	:						Optional[int]       =       np.array([tree.predict(lowercase__							) for x in test_cases]							)
     UpperCAmelCase__	:						Optional[Any]       =       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)










"""simple docstring"""



def   SCREAMING_SNAKE_CASE__					(     snake_case					:   int							)->		list:






     '''simple docstring'''



     UpperCAmelCase__	:						List[str]       =       [True for i in range(n + 1							)]
     UpperCAmelCase__	:						List[str]       =       [i - 1 for i in range(n + 1							)]
     UpperCAmelCase__	:						int       =       []
     for i in range(2 ,				n + 1							):
          if is_prime[i]:
               primes.append(snake_case							)
          for j in range(0 ,				len(snake_case							)							):
               if i * primes[j] >= n:
                    break
               UpperCAmelCase__	:						Optional[Any]       =       False

               if i % primes[j] == 0:
                    UpperCAmelCase__	:						str       =       totients[i] * primes[j]
                    break

               UpperCAmelCase__	:						Union[str, Any]       =       totients[i] * (primes[j] - 1)

     return totients


if __name__ == "__main__":
 import doctest

 doctest.testmod()


