

import numpy as np







class 						lowercase     :


		"""simple docstring"""




		def __init__(					self						,							__snake_case=5						,							__snake_case=5):
			_UpperCamelCase :		List[Any]								=    depth
			_UpperCamelCase :		List[str]								=    0
			_UpperCamelCase :		Any								=    None
			_UpperCamelCase :		List[Any]								=    None
			_UpperCamelCase :		Tuple								=    min_leaf_size
			_UpperCamelCase :		Any								=    None


		def 		A__       (					self						,							__snake_case						,							__snake_case):
			if labels.ndim != 1:
				print('Error: Input labels must be one dimensional')

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


		def 		A__       (					self						,							__snake_case						,							__snake_case):
			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:
				_UpperCamelCase :		Tuple								=    np.mean(_A)
				return

			if self.depth == 1:
				_UpperCamelCase :		Tuple								=    np.mean(_A)
				return

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

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

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

			return






		def 		A__       (					self						,							__snake_case):
			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 						lowercase     :


		"""simple docstring"""




		@staticmethod
		def 		A__       (					__snake_case						,							__snake_case):
			_UpperCamelCase :		str								=    float(0)
			for label in labels:
				squared_error_sum += (label - prediction) ** 2

			return float(squared_error_sum / labels.size)






def        lowerCamelCase_  (				)    ->					List[str]:




	'''simple docstring'''




	_UpperCamelCase :		Any								=    np.arange(-1.0				,							1.0				,							0.0_0_5						)
	_UpperCamelCase :		Optional[int]								=    np.sin(UpperCAmelCase__						)

	_UpperCamelCase :		Any								=    DecisionTree(depth=1_0				,							min_leaf_size=1_0						)
	tree.train(UpperCAmelCase__				,							UpperCAmelCase__						)

	_UpperCamelCase :		str								=    (np.random.rand(1_0						) * 2) - 1
	_UpperCamelCase :		List[str]								=    np.array([tree.predict(UpperCAmelCase__						) for x in test_cases]						)
	_UpperCamelCase :		List[Any]								=    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)







import random


def 							__lowercase     (	UpperCAmelCase__   ):

					"""simple docstring"""






					def is_sorted(UpperCAmelCase__   ):
										for i in range(len(UpperCAmelCase__   ) - 1   ):
															if collection[i] > collection[i + 1]:
																				return False
										return True

					while not is_sorted(UpperCAmelCase__   ):
										random.shuffle(UpperCAmelCase__   )
					return collection


if __name__ == "__main__":
		lowerCamelCase   					=  input('''Enter numbers separated by a comma:\n''').strip()
		lowerCamelCase   					=  [int(item) for item in user_input.split(''',''')]
		print(bogo_sort(unsorted))






