






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					(						a_	)	->			Union[str, Any]:
							return list(a_	)


if __name__ == "__main__":
		import doctest

		doctest.testmod()











import math
def      lowerCamelCase					(						a_	)	->			Dict:
							lowerCAmelCase_										=     0
							if n % 2 == 0:
														count += 1
														while n % 2 == 0:
																					lowerCAmelCase_										=     int(n / 2	)
    # the n input value must be odd so that
    # we can skip one element (ie i += 2)

							lowerCAmelCase_										=     3

							while i <= int(math.sqrt(a_	)	):
														if n % i == 0:
																					count += 1
																					while n % i == 0:
																												lowerCAmelCase_										=     int(n / i	)
														lowerCAmelCase_										=     i + 2

							# this condition checks the prime
							# number n is greater than 2

							if n > 2:
														count += 1
							return count


if __name__ == "__main__":
		lowerCamelCase_						     =  5_1_2_4_2_1_8_3
		print(f'''The number of distinct prime factors is/are {exact_prime_factor_count(n)}''')
		print(f'''The value of log(log(n)) is {math.log(math.log(n)):.4f}''')



