def test_knapsack_empty(self):
bag = ()
size = 50
ks = Knapsack(bag, size)
result = ks.solve()
self.assertEqual((), result)
def test_knapsack_values(self):
bag = (
(10, 60),
(20, 100),
(30, 195),
(40, 120),
(50, 120),
(5, 120),
(60, 120),
(10, 50),
(10, 200),
(20, 120),
)
size = 50
ks = Knapsack(bag, size)
result = ks.solve()
self.assertEqual(((30, 195), (5, 120), (10, 200)), result)
media7 = Curve(664196.7, 0.662257, 0.92, 1.275716e-06 / 87.98509)
# adding media to budget
# in second argument we pass minimum and maximum allowed for media
budget.add_curve(media1, 10600000, maxb)
budget.add_curve(media2, 5700000, maxb)
budget.add_curve(media3, 1923077, maxb)
budget.add_curve(media4, 8307692, maxb)
budget.add_curve(media5, 3246154, maxb)
budget.add_curve(media6, 9791667, maxb)
budget.add_curve(media7, 15786389, maxb)
# call to optimize spends between media
# last line represents optimal budget for every media, e.g. first number
# stands for media1, second for media2, etc.
budget.solve()
"""
Optimization terminated successfully.(Exit mode 0)
Current function value: -1.0779430709417468
Iterations: 2
Function evaluations: 2
Gradient evaluations: 2
Out[37]:
array([7917025.28571427, 4140102.28571439, 10524717.28571427,
5463179.28571427, 12008692.28571427, 12817025.28571427,
18003414.28571427])
"""
print(budget.solution)
# Out[38]: 1.0779430709417468
# Old solution found with old optimization method
print(