



import numpy as np







class 							_a							:

			def __init__(self,      SCREAMING_SNAKE_CASE_=5,      SCREAMING_SNAKE_CASE_=5				)					->      Dict:
						UpperCAmelCase_:		Optional[Any]       			=    depth
						UpperCAmelCase_:		List[str]       			=    0
						UpperCAmelCase_:		Union[str, Any]       			=    None
						UpperCAmelCase_:		Union[str, Any]       			=    None
						UpperCAmelCase_:		List[Any]       			=    min_leaf_size
						UpperCAmelCase_:		Dict       			=    None

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)					->      int:
						if labels.ndim != 1:
									print("""Error: Input labels must be one dimensional"""				)

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

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)					->      Optional[int]:
						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:
									UpperCAmelCase_:		List[str]       			=    np.mean(SCREAMING_SNAKE_CASE_				)
									return

						if self.depth == 1:
									UpperCAmelCase_:		Union[str, Any]       			=    np.mean(SCREAMING_SNAKE_CASE_				)
									return

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

						if best_split != 0:
									UpperCAmelCase_:		Optional[int]       			=    x[:best_split]
									UpperCAmelCase_:		Dict       			=    y[:best_split]
									UpperCAmelCase_:		List[Any]       			=    x[best_split:]
									UpperCAmelCase_:		Union[str, Any]       			=    y[best_split:]

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

						return

			def     __snake_case					(self,      SCREAMING_SNAKE_CASE_				)					->      int:
						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_,      SCREAMING_SNAKE_CASE_				)					->      Optional[Any]:
						UpperCAmelCase_:		List[Any]       			=    float(0				)
						for label in labels:
									squared_error_sum += (label - prediction) ** 2

						return float(squared_error_sum / labels.size				)







def    lowerCAmelCase_  ():





			"""simple docstring"""


			UpperCAmelCase_:		Optional[int]       			=    np.arange(-1.0		,	1.0		,	0.005     )
			UpperCAmelCase_:		List[Any]       			=    np.sin(lowerCAmelCase__     )

			UpperCAmelCase_:		Tuple       			=    DecisionTree(depth=1_0		,	min_leaf_size=1_0     )
			tree.train(lowerCAmelCase__		,	lowerCAmelCase__     )

			UpperCAmelCase_:		Tuple       			=    (np.random.rand(1_0     ) * 2) - 1
			UpperCAmelCase_:		str       			=    np.array([tree.predict(lowerCAmelCase__     ) for x in test_cases]     )
			UpperCAmelCase_:		Optional[int]       			=    np.mean((predictions - test_cases) ** 2     )

			print("""Test values: """ + str(lowerCAmelCase__     )     )
			print("""Predictions: """ + str(lowerCAmelCase__     )     )
			print("""Average error: """ + str(lowerCAmelCase__     )     )


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

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











def    lowerCAmelCase_  (lowerCAmelCase__:		str     ):





			"""simple docstring"""


			return "".join(chr(ord(lowerCAmelCase__     ) + 3_2     ) if """A""" <= char <= """Z""" else char for char in word     )


if __name__ == "__main__":
	from doctest import testmod

	testmod()






