
from sklearn.neural_network import MLPClassifier

UpperCamelCase_  =	[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
UpperCamelCase_  =	[0, 1, 0, 0]


UpperCamelCase_  =	MLPClassifier(
    solver="lbfgs", alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


UpperCamelCase_  =	[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
UpperCamelCase_  =	clf.predict(test)





def 							_lowerCamelCase			(					lowerCamelCase_:  Tuple		):



				'''simple docstring'''







				return list(lowerCamelCase_		)


if __name__ == "__main__":
					import doctest

					doctest.testmod()

def 							_lowerCamelCase			(					lowerCamelCase_:  list		):



				'''simple docstring'''







				A     :  Optional[int]   =				1.3
				A     :  List[str]   =				len(lowerCamelCase_		)
				A     :  Optional[Any]   =				False

				while not completed:
								# Update the gap value for a next comb
								A     :  str   =				int(gap / shrink_factor		)
								if gap <= 1:
												A     :  Tuple   =				True

								A     :  List[Any]   =				0
								while index + gap < len(lowerCamelCase_		):
												if data[index] > data[index + gap]:
																# Swap values
																A ,       A     :  int   =				data[index + gap], data[index]
																A     :  Dict   =				False
												index += 1

				return data


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					UpperCamelCase_  =	input("Enter numbers separated by a comma:\n").strip()
					UpperCamelCase_  =	[int(item) for item in user_input.split(",")]
					print(comb_sort(unsorted))