



'''simple docstring'''







from sklearn.neural_network import MLPClassifier

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


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

clf.fit(X, y)


SCREAMING_SNAKE_CASE__     : Any				=   [[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
SCREAMING_SNAKE_CASE__     : Any				=   clf.predict(test)







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE     ):
		return list(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
							import doctest

							doctest.testmod()


def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: Union[str, Any]       ,				SCREAMING_SNAKE_CASE			: Any     )	->		Tuple:
				# "Simpson Rule"
				# int(f) = delta_x/2 * (b-a)/3*(f1 + 4f2 + 2f_3 + ... + fn)
				__lowercase								=   (boundary[1] - boundary[0]) / steps
				__lowercase								=   boundary[0]
				__lowercase								=   boundary[1]
				__lowercase								=   make_points(SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE     )
				__lowercase								=   0.0
				y += (h / 3.0) * f(SCREAMING_SNAKE_CASE     )
				__lowercase								=   2
				for i in x_i:
								y += (h / 3) * (4 - 2 * (cnt % 2)) * f(SCREAMING_SNAKE_CASE     )
								cnt += 1
				y += (h / 3.0) * f(SCREAMING_SNAKE_CASE     )
				return y




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: List[Any]       ,				SCREAMING_SNAKE_CASE			: Dict       ,				SCREAMING_SNAKE_CASE			: int     )	->		Any:
				__lowercase								=   a + h
				while x < (b - h):
								yield x
								__lowercase								=   x + h




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: Any     )	->		List[Any]:  # enter your function here
				__lowercase								=   (x - 0) * (x - 0)
				return y




def 				__SCREAMING_SNAKE_CASE  (						)	->		List[str]:
				__lowercase								=   0.0  # Lower bound of integration
				__lowercase								=   1.0  # Upper bound of integration
				__lowercase								=   10.0  # define number of steps or resolution
				__lowercase								=   [a, b]  # define boundary of integration
				__lowercase								=   method_a(SCREAMING_SNAKE_CASE       ,				SCREAMING_SNAKE_CASE     )
				print(F"""y = {y}"""     )


if __name__ == "__main__":
							main()





