



from sklearn.neural_network import MLPClassifier

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


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

clf.fit(X, y)


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






def 						_lowerCAmelCase    (					__lowerCamelCase :  Union[str, Any]  ):



 """simple docstring"""





 return list(__lowerCamelCase  )


if __name__ == "__main__":
      import doctest

      doctest.testmod()









from __future__ import annotations




def        __UpperCamelCase     (      _A					:							int      ,  _A					:							list[int]      ,  _A					:							list[int]      ,  _A					:							int		)				->int:



							"""simple docstring"""

							# Base Case
							if counter == 0 or capacity == 0:
														return 0

							# If weight of the nth item is more than Knapsack of capacity,
							#   then this item cannot be included in the optimal solution,
							# else return the maximum of two cases:
							#   (1) nth item included
							#   (2) not included
							if weights[counter - 1] > capacity:
														return knapsack(_A      ,  _A      ,  _A      ,  counter - 1		)
							else:
														lowerCamelCase_								=capacity - weights[counter - 1]
														lowerCamelCase_								=values[counter - 1] + knapsack(
														    _A      ,  _A      ,  _A      ,  counter - 1		)
														lowerCamelCase_								=knapsack(_A      ,  _A      ,  _A      ,  counter - 1		)
														return max(_A      ,  _A		)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
