



'''simple docstring'''



import numpy as np






class 			_SCREAMING_SNAKE_CASE				:



			def __init__(     self		:						str    ,			__UpperCamelCase		:						List[Any]=5    ,			__UpperCamelCase		:						Union[str, Any]=5			)      ->      List[str]:
							"""simple docstring"""





							snake_case__      :   Any      =    depth
							snake_case__      :   Optional[int]      =    0
							snake_case__      :   str      =    None
							snake_case__      :   int      =    None
							snake_case__      :   List[str]      =    min_leaf_size
							snake_case__      :   int      =    None



			def 				lowerCAmelCase (     self		:						Optional[int]    ,			__UpperCamelCase		:						int    ,			__UpperCamelCase		:						Optional[Any]			)      ->      str:
							"""simple docstring"""





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

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



			def 				lowerCAmelCase (     self		:						Union[str, Any]    ,			__UpperCamelCase		:						Optional[int]    ,			__UpperCamelCase		:						Any			)      ->      Union[str, Any]:
							"""simple docstring"""





							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__      :   Union[str, Any]      =    np.mean(_UpperCamelCase			)
											return

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

							snake_case__      :   List[Any]      =    0
							snake_case__      :   Dict      =    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__      :   List[str]      =    self.mean_squared_error(x[:i]    ,			np.mean(y[:i]			)			)
															snake_case__      :   Tuple      =    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__      :   Optional[Any]      =    i
																			snake_case__      :   List[Any]      =    error

							if best_split != 0:
											snake_case__      :   List[str]      =    x[:best_split]
											snake_case__      :   Tuple      =    y[:best_split]
											snake_case__      :   Dict      =    x[best_split:]
											snake_case__      :   List[Any]      =    y[best_split:]

											snake_case__      :   List[str]      =    x[best_split]
											snake_case__      :   List[str]      =    DecisionTree(
											    depth=self.depth - 1    ,			min_leaf_size=self.min_leaf_size			)
											snake_case__      :   List[Any]      =    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__      :   List[Any]      =    np.mean(_UpperCamelCase			)

							return







			def 				lowerCAmelCase (     self		:						List[str]    ,			__UpperCamelCase		:						Union[str, Any]			)      ->      Tuple:
							"""simple docstring"""





							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		:						Optional[Any]    ,			__UpperCamelCase		:						Any			)      ->      Union[str, Any]:
							"""simple docstring"""





							snake_case__      :   Optional[Any]      =    float(0			)
							for label in labels:
											squared_error_sum += (label - prediction) ** 2

							return float(squared_error_sum / labels.size			)



def 							__UpperCAmelCase				(				)		->						List[Any]:
				snake_case__      :   Tuple      =    np.arange(-1.0   ,				1.0   ,				0.005			)
				snake_case__      :   Tuple      =    np.sin(UpperCamelCase_			)

				snake_case__      :   Any      =    DecisionTree(depth=10   ,				min_leaf_size=10			)
				tree.train(UpperCamelCase_   ,				UpperCamelCase_			)

				snake_case__      :   List[str]      =    (np.random.rand(10			) * 2) - 1
				snake_case__      :   str      =    np.array([tree.predict(UpperCamelCase_			) for x in test_cases]			)
				snake_case__      :   str      =    np.mean((predictions - test_cases) ** 2			)

				print('''Test values: ''' + str(UpperCamelCase_			)			)
				print('''Predictions: ''' + str(UpperCamelCase_			)			)
				print('''Average error: ''' + str(UpperCamelCase_			)			)


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

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












from __future__ import annotations







def 						_UpperCamelCase (     UpperCamelCase_							:       list[int]		)	-> list[int]:

						"""simple docstring"""



						if not list_of_ints:
												return []

						if min(UpperCamelCase_		) < 0:
												raise ValueError('All numbers must be positive'		)

						lowerCAmelCase__         =    max(len(bin(UpperCamelCase_		)[2:]		) for x in list_of_ints		)
						return _msd_radix_sort(UpperCamelCase_     ,							UpperCamelCase_		)







def 						_UpperCamelCase (     UpperCamelCase_							:       list[int]     ,							UpperCamelCase_							:       int		)	-> list[int]:

						"""simple docstring"""



						if bit_position == 0 or len(UpperCamelCase_		) in [0, 1]:
												return list_of_ints

						lowerCAmelCase__         =    []
						lowerCAmelCase__         =    []
						# Split numbers based on bit at bit_position from the right
						for number in list_of_ints:
												if (number >> (bit_position - 1)) & 1:
																		# number has a one at bit bit_position
																		ones.append(UpperCamelCase_		)
												else:
																		# number has a zero at bit bit_position
																		zeros.append(UpperCamelCase_		)

    # recursively split both lists further
						lowerCAmelCase__         =    _msd_radix_sort(UpperCamelCase_     ,							bit_position - 1		)
						lowerCAmelCase__         =    _msd_radix_sort(UpperCamelCase_     ,							bit_position - 1		)

						# recombine lists
						lowerCAmelCase__         =    zeros
						res.extend(UpperCamelCase_		)

						return res







def 						_UpperCamelCase (     UpperCamelCase_							:       list[int]		)	-> Any:

						"""simple docstring"""



						lowerCAmelCase__         =    len(UpperCamelCase_		)
						if not list_of_ints or length == 1:
												return

						if min(UpperCamelCase_		) < 0:
												raise ValueError('All numbers must be positive'		)

						lowerCAmelCase__         =    max(len(bin(UpperCamelCase_		)[2:]		) for x in list_of_ints		)
						_msd_radix_sort_inplace(UpperCamelCase_     ,							UpperCamelCase_     ,							0     ,							UpperCamelCase_		)







def 						_UpperCamelCase (     UpperCamelCase_							:       list[int]     ,							UpperCamelCase_							:       int     ,							UpperCamelCase_							:       int     ,							UpperCamelCase_							:       int		)	-> int:

						"""simple docstring"""



						if bit_position == 0 or end_index - begin_index <= 1:
												return

						bit_position -= 1

						lowerCAmelCase__         =    begin_index
						lowerCAmelCase__         =    end_index - 1
						while i <= j:
												lowerCAmelCase__         =    False
												if not (list_of_ints[i] >> bit_position) & 1:
																		# found zero at the beginning
																		i += 1
																		lowerCAmelCase__         =    True
												if (list_of_ints[j] >> bit_position) & 1:
																		# found one at the end
																		j -= 1
																		lowerCAmelCase__         =    True

												if changed:
																		continue

												lowerCAmelCase__    ,							lowerCAmelCase__         =    list_of_ints[j], list_of_ints[i]
												j -= 1
												if j != i:
																		i += 1

						_msd_radix_sort_inplace(UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_		)
						_msd_radix_sort_inplace(UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_     ,							UpperCamelCase_		)


if __name__ == "__main__":
					import doctest

					doctest.testmod()





