


from sklearn.neural_network import MLPClassifier

UpperCAmelCase      :						str    =		[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
UpperCAmelCase      :						Union[str, Any]    =		[0, 1, 0, 0]


UpperCAmelCase      :						str    =		MLPClassifier(
    solver="lbfgs", alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


UpperCAmelCase      :						str    =		[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
UpperCAmelCase      :						Optional[int]    =		clf.predict(test)






def   __lowerCamelCase	(       lowerCamelCase__		:	List[str]			):





			'''simple docstring'''




			return list(lowerCamelCase__			)


if __name__ == "__main__":
		import doctest

		doctest.testmod()





def   __lowerCamelCase	(       lowerCamelCase__		:	int = 100     ,       lowerCamelCase__		:	int = 100			):





			'''simple docstring'''




			return max(
			    sum(int(lowerCamelCase__			) for x in str(base**power			)			)
			    for base in range(lowerCamelCase__			)
			    for power in range(lowerCamelCase__			)			)


# Tests
if __name__ == "__main__":
		import doctest

		doctest.testmod()

