


from sklearn.neural_network import MLPClassifier

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


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

clf.fit(X, y)


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



def      lowerCAmelCase	(						lowerCAmelCase_	)->	Any:
				return list(SCREAMING_SNAKE_CASE_	)


if __name__ == "__main__":
						import doctest

						doctest.testmod()



from __future__ import annotations



def      lowerCAmelCase	(						lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	)->	int | float:
				if len(lowerCAmelCase_	) == 0:
								raise ValueError('''find_min() arg is an empty sequence'''	)
				if (
				    left >= len(lowerCAmelCase_	)
				    or left < -len(lowerCAmelCase_	)
				    or right >= len(lowerCAmelCase_	)
				    or right < -len(lowerCAmelCase_	)
				):
								raise IndexError('''list index out of range'''	)
				if left == right:
								return nums[left]
				lowerCAmelCase_	: Any      			=			(left + right) >> 1  # the middle
				lowerCAmelCase_	: Tuple      			=			find_min(lowerCAmelCase_	,				lowerCAmelCase_	,				lowerCAmelCase_	)  # find min in range[left, mid]
				lowerCAmelCase_	: Any      			=			find_min(lowerCAmelCase_	,				mid + 1	,				lowerCAmelCase_	)  # find min in range[mid + 1, right]

				return left_min if left_min <= right_min else right_min


if __name__ == "__main__":
						import doctest

						doctest.testmod(verbose=True)