def test_01_02(self):
r = np.random.RandomState()
r.seed(11)
for reductions in [0, 2]:
for _ in range(100):
c = r.randint(1, 10, (5, 5))
i, j = np.mgrid[0:5, 0:5]
i = i.flatten()
j = j.flatten()
x, y, u, v = LAPJV.lapjv(i, j, c.flatten(), True, reductions)
min_cost = np.sum(c)
best = None
for permutation in permutations([0, 1, 2, 3, 4]):
cost = sum([c[i, permutation[i]] for i in range(5)])
if cost < min_cost:
best = list(permutation)
min_cost = cost
result_cost = sum([c[i, x[i]] for i in range(5)])
self.assertAlmostEqual(min_cost, result_cost)
def test_01_02(self):
r = np.random.RandomState()
r.seed(11)
for reductions in [0,2]:
for _ in range(100):
c = r.randint(1,10,(5,5))
i,j = np.mgrid[0:5,0:5]
i = i.flatten()
j = j.flatten()
x,y,u,v = LAPJV.lapjv(i, j, c.flatten(), True, reductions)
min_cost = np.sum(c)
best = None
for permutation in permutations([0,1,2,3,4]):
cost = sum([c[i,permutation[i]] for i in range(5)])
if cost < min_cost:
best = list(permutation)
min_cost = cost
result_cost = sum([c[i,x[i]] for i in range(5)])
self.assertAlmostEqual(min_cost, result_cost)