






import numpy as np


class 					__SCREAMING_SNAKE_CASE:
							def __init__(       self: Optional[Any]    ,		UpperCamelCase: str=5    ,		UpperCamelCase: Optional[Any]=5  )						->						int:
											snake_case__													=       depth
											snake_case__													=       0
											snake_case__													=       None
											snake_case__													=       None
											snake_case__													=       min_leaf_size
											snake_case__													=       None
							def lowerCAmelCase_	(       self: List[str]    ,		UpperCamelCase: List[str]    ,		UpperCamelCase: Any  )						->						Union[str, Any]:
											if labels.ndim != 1:
															print('Error: Input labels must be one dimensional'  )

											return np.mean((labels - prediction) ** 2  )
							def lowerCAmelCase_	(       self: Dict    ,		UpperCamelCase: List[str]    ,		UpperCamelCase: List[str]  )						->						Tuple:
											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:
															snake_case__													=       np.mean(UpperCamelCase  )
															return

											if self.depth == 1:
															snake_case__													=       np.mean(UpperCamelCase  )
															return

											snake_case__													=       0
											snake_case__													=       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:
																			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(UpperCamelCase    ,		UpperCamelCase  )
															self.right.train(UpperCamelCase    ,		UpperCamelCase  )
											else:
															snake_case__													=       np.mean(UpperCamelCase  )

											return


							def lowerCAmelCase_	(       self: Optional[int]    ,		UpperCamelCase: Optional[Any]  )						->						Dict:
											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 					__SCREAMING_SNAKE_CASE:
							@staticmethod
							def lowerCAmelCase_	(       UpperCamelCase: Dict    ,		UpperCamelCase: int  )						->						List[Any]:
											snake_case__													=       float(0  )
											for label in labels:
															squared_error_sum += (label - prediction) ** 2

											return float(squared_error_sum / labels.size  )


def        a_	( )     ->     Dict:







				"""simple docstring"""
				snake_case__													=       np.arange(-1.0 ,				1.0 ,				0.005					)
				snake_case__													=       np.sin(_A					)

				snake_case__													=       DecisionTree(depth=10 ,				min_leaf_size=10					)
				tree.train(_A ,				_A					)

				snake_case__													=       (np.random.rand(10					) * 2) - 1
				snake_case__													=       np.array([tree.predict(_A					) for x in test_cases]					)
				snake_case__													=       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        a_	( _A ,				_A					)     ->     Union[str, Any]:







				"""simple docstring"""
				snake_case__													=       1
				while b > 0:
								if b & 1:
												res *= a

								a *= a
								b >>= 1

				return res
def        a_	( _A ,				_A ,				_A					)     ->     Dict:







				"""simple docstring"""
				snake_case__													=       1
				while b > 0:
								if b & 1:
												snake_case__													=       ((res % c) * (a % c)) % c

								a *= a
								b >>= 1

				return res




