
'''simple docstring'''





import numpy as np

class     a :




				def __init__(				self						:					Any,     SCREAMING_SNAKE_CASE_						:					int=5,     SCREAMING_SNAKE_CASE_						:					Dict=5   ):
										snake_case							:  Optional[Any]			=     depth
										snake_case							:  int			=     0
										snake_case							:  List[str]			=     None
										snake_case							:  Tuple			=     None
										snake_case							:  List[Any]			=     min_leaf_size
										snake_case							:  List[Any]			=     None




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

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




				def 						__snake_case				(				self						:					List[Any],     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					Tuple   ):
										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							:  Tuple			=     np.mean(SCREAMING_SNAKE_CASE_   )
																return

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

										snake_case							:  List[str]			=     0
										snake_case							:  Any			=     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							:  str			=     self.mean_squared_error(x[:i],     np.mean(y[:i]   )   )
																						snake_case							:  List[str]			=     self.mean_squared_error(x[i:],     np.mean(y[i:]   )   )
																						snake_case							:  Union[str, Any]			=     error_left + error_right
																						if error < min_error:
																												snake_case							:  Union[str, Any]			=     i
																												snake_case							:  Any			=     error

										if best_split != 0:
																snake_case							:  Union[str, Any]			=     x[:best_split]
																snake_case							:  Union[str, Any]			=     y[:best_split]
																snake_case							:  Any			=     x[best_split:]
																snake_case							:  List[str]			=     y[best_split:]

																snake_case							:  List[str]			=     x[best_split]
																snake_case							:  Optional[int]			=     DecisionTree(
																    depth=self.depth - 1,     min_leaf_size=self.min_leaf_size   )
																snake_case							:  str			=     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							:  Union[str, Any]			=     np.mean(SCREAMING_SNAKE_CASE_   )

										return




				def 						__snake_case				(				self						:					Tuple,     SCREAMING_SNAKE_CASE_						:					List[str]   ):
										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     a :




				@staticmethod
				def 						__snake_case				(				SCREAMING_SNAKE_CASE_						:					Tuple,     SCREAMING_SNAKE_CASE_						:					int   ):
										snake_case							:  Optional[Any]			=     float(0   )
										for label in labels:
																squared_error_sum += (label - prediction) ** 2

										return float(squared_error_sum / labels.size   )




def   A			(							):
						snake_case							:  Union[str, Any]			=     np.arange(-1.0     ,				1.0     ,				0.005							)
						snake_case							:  Optional[Any]			=     np.sin(A_							)

						snake_case							:  List[Any]			=     DecisionTree(depth=10     ,				min_leaf_size=10							)
						tree.train(A_     ,				A_							)

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





def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   )->    list:




      """simple docstring"""

      snake_case_			= [True for i in range(n + 1   )]
      snake_case_			= [i - 1 for i in range(n + 1   )]
      snake_case_			= []
      for i in range(2   ,   n + 1   ):
            if is_prime[i]:
                  primes.append(SCREAMING_SNAKE_CASE   )
            for j in range(0   ,   len(SCREAMING_SNAKE_CASE   )   ):
                  if i * primes[j] >= n:
                        break
                  snake_case_			= False

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

                  snake_case_			= totients[i] * (primes[j] - 1)

      return totients


if __name__ == "__main__":
   import doctest

   doctest.testmod()