
import numpy as np
class    _UpperCamelCase:

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

						def 						a__ (					self		:			List[Any]	,						_lowerCamelCase		:			Dict	,						_lowerCamelCase		:			Dict			):


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

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

						def 						a__ (					self		:			Tuple	,						_lowerCamelCase		:			Dict	,						_lowerCamelCase		:			List[Any]			):


													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:
																				_UpperCAmelCase  :			Dict	       =		np.mean(_UpperCAmelCase			)
																				return

													if self.depth == 1:
																				_UpperCAmelCase  :			Any	       =		np.mean(_UpperCAmelCase			)
																				return

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

													if best_split != 0:
																				_UpperCAmelCase  :			Optional[Any]	       =		x[:best_split]
																				_UpperCAmelCase  :			Any	       =		y[:best_split]
																				_UpperCAmelCase  :			List[str]	       =		x[best_split:]
																				_UpperCAmelCase  :			int	       =		y[best_split:]

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

													return






						def 						a__ (					self		:			List[str]	,						_lowerCamelCase		:			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(_UpperCAmelCase			)
																				else:
																											return self.left.predict(_UpperCAmelCase			)
													else:
																				print("Error: Decision tree not yet trained"			)
																				return None







class    _UpperCamelCase:

						@staticmethod
						def 						a__ (					_lowerCamelCase		:			int	,						_lowerCamelCase		:			Dict			):
													_UpperCAmelCase  :			List[str]	       =		float(0			)
													for label in labels:
																				squared_error_sum += (label - prediction) ** 2

													return float(squared_error_sum / labels.size			)







def 		lowerCAmelCase_     (     )   ->      List[Any]:

							"""simple docstring"""






							_UpperCAmelCase  :			Union[str, Any]	       =		np.arange(-1.0      ,	1.0      ,	0.005  )
							_UpperCAmelCase  :			Tuple	       =		np.sin(_SCREAMING_SNAKE_CASE  )

							_UpperCAmelCase  :			Optional[int]	       =		DecisionTree(depth=1_0      ,	min_leaf_size=1_0  )
							tree.train(_SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE  )

							_UpperCAmelCase  :			Dict	       =		(np.random.rand(1_0  ) * 2) - 1
							_UpperCAmelCase  :			Union[str, Any]	       =		np.array([tree.predict(_SCREAMING_SNAKE_CASE  ) for x in test_cases]  )
							_UpperCAmelCase  :			Union[str, Any]	       =		np.mean((predictions - test_cases) ** 2  )

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


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

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






from ..utils import DummyObject, requires_backends
class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Tuple            =				["torch", "transformers", "invisible_watermark"]





							def __init__(					self    :  Any	,					*_UpperCAmelCase    :  Optional[Any]	,					**_UpperCAmelCase    :  List[str]							)		->						Tuple:



											"""simple docstring"""


											requires_backends(self	,					['torch', 'transformers', 'invisible_watermark']							)





							@classmethod
							def      a__					(					cls    :  str	,					*_UpperCAmelCase    :  List[str]	,					**_UpperCAmelCase    :  Optional[Any]							)		->						Union[str, Any]:



											"""simple docstring"""


											requires_backends(cls	,					['torch', 'transformers', 'invisible_watermark']							)





							@classmethod
							def      a__					(					cls    :  List[Any]	,					*_UpperCAmelCase    :  List[Any]	,					**_UpperCAmelCase    :  Union[str, Any]							)		->						str:



											"""simple docstring"""


											requires_backends(cls	,					['torch', 'transformers', 'invisible_watermark']							)






class       A__  (		metaclass=lowerCAmelCase__							):
							lowerCAmelCase__						:		Tuple            =				["torch", "transformers", "invisible_watermark"]





							def __init__(					self    :  Dict	,					*_UpperCAmelCase    :  Optional[int]	,					**_UpperCAmelCase    :  Any							)		->						Union[str, Any]:



											"""simple docstring"""


											requires_backends(self	,					['torch', 'transformers', 'invisible_watermark']							)





							@classmethod
							def      a__					(					cls    :  List[str]	,					*_UpperCAmelCase    :  Union[str, Any]	,					**_UpperCAmelCase    :  Optional[Any]							)		->						Optional[int]:



											"""simple docstring"""


											requires_backends(cls	,					['torch', 'transformers', 'invisible_watermark']							)





							@classmethod
							def      a__					(					cls    :  Tuple	,					*_UpperCAmelCase    :  List[Any]	,					**_UpperCAmelCase    :  Tuple							)		->						Any:



											"""simple docstring"""


											requires_backends(cls	,					['torch', 'transformers', 'invisible_watermark']							)





