



import numpy as np

class 			__snake_case   :







							'''simple docstring'''






							def __init__(				self	,	a_=5	,	a_=5		):
											a__					   =		depth
											a__					   =		0
											a__					   =		None
											a__					   =		None
											a__					   =		min_leaf_size
											a__					   =		None


							def _a   (				self	,	a_	,	a_		):
											if labels.ndim != 1:
															print("""Error: Input labels must be one dimensional"""		)

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


							def _a   (				self	,	a_	,	a_		):
											if x.ndim != 1:
															print("""Error: Input data set must be one dimensional"""		)
															return
											if len(a_		) != len(a_		):
															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(a_		) < 2 * self.min_leaf_size:
															a__					   =		np.mean(a_		)
															return

											if self.depth == 1:
															a__					   =		np.mean(a_		)
															return

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

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

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

											return







							def _a   (				self	,	a_		):
											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(a_		)
															else:
																			return self.left.predict(a_		)
											else:
															print("""Error: Decision tree not yet trained"""		)
															return None




class 			__snake_case   :







							'''simple docstring'''






							@staticmethod
							def _a   (				a_	,	a_		):
											a__					   =		float(0		)
											for label in labels:
															squared_error_sum += (label - prediction) ** 2

											return float(squared_error_sum / labels.size		)




def       A_   (						):

				"""simple docstring"""
				a__					   =		np.arange(-1.0			,		1.0			,		0.0_0_5		)
				a__					   =		np.sin(__a		)

				a__					   =		DecisionTree(depth=10			,		min_leaf_size=10		)
				tree.train(__a			,		__a		)

				a__					   =		(np.random.rand(10		) * 2) - 1
				a__					   =		np.array([tree.predict(__a		) for x in test_cases]		)
				a__					   =		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   _lowercase       (    _UpperCAmelCase					)    ->	bool:
				if not isinstance(_UpperCAmelCase      ,   _UpperCAmelCase					):
								lowerCamelCase									=F"""Input value of [number={number}] must be an integer"""
								raise TypeError(_UpperCAmelCase					)
				if number < 0 or number % 2 == 1:
								return False
				lowerCamelCase									=int(number**0.5					)
				return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
							import doctest

							doctest.testmod()





