def murty(C):
"""Algorithm due to Murty."""
try:
Q = queue.PriorityQueue()
M = C.shape[0]
N = C.shape[1]
cost, assign = lap(C)[0:2]
Q.put((cost, list(assign), (), (), (), ()))
k = 0
while not Q.empty():
S = Q.get_nowait()
yield (S[0], S[1][:M])
k += 1
ni = len(S[2])
rmap = tuple(x for x in range(M) if x not in S[2])
cmap = tuple(x for x in S[1] if x not in S[3])
cmap += tuple(x for x in range(N)
if x not in S[3] and x not in S[1])
removed_values = C[S[4], S[5]]
C[S[4], S[5]] = LARGE
C_ = C[rmap, :][:, cmap]
for t in range(M - ni):
removed_value = C_[t, t]
C_[t, t] = LARGE
cost, lassign = lap(C_[t:, t:])[0:2]
if LARGE not in C_[range(t, t + len(lassign)), lassign + t]:
cost += C[S[2], S[3]].sum()
cost += C_[range(t), range(t)].sum()
assign = [None] * M
for r in range(ni):
assign[S[2][r]] = S[3][r]
for r in range(t):
assign[rmap[r]] = cmap[r]
for r in range(len(lassign)):
assign[rmap[r + t]] = cmap[lassign[r] + t]
nxt = (cost, assign, S[2] + tuple(rmap[x]
for x in range(t)),
S[3] + tuple(cmap[:t]), S[4] + (rmap[t], ),
S[5] + (cmap[t], ))
Q.put(nxt)
C_[t, t] = removed_value
C[S[4], S[5]] = removed_values
except GeneratorExit:
pass
def test_lap_square():
ret = lapjv.lap(cost)
assert ret[0] == 17.0
assert np.all(ret[1] == [1, 2, 0, 4, 5, 3, 7, 6])
assert np.all(ret[2] == [2, 0, 1, 5, 3, 4, 7, 6])
assert cost[range(cost.shape[0]), ret[1]].sum() == ret[0]
assert cost[ret[2], range(cost.shape[1])].sum() == ret[0]
def test_lap_duals():
ret = lapjv.lap(cost, u=np.zeros((cost.shape[0],), dtype=np.double))
assert ret[0] == 17.0
assert np.all(ret[1] == [1, 2, 0, 4, 5, 3, 7, 6])
assert np.all(ret[2] == [2, 0, 1, 5, 3, 4, 7, 6])
assert cost[range(cost.shape[0]), ret[1]].sum() == ret[0]
assert cost[ret[2], range(cost.shape[1])].sum() == ret[0]
def test_lap_cost_eps():
# This test should just return.
cost = np.genfromtxt(GzipFile('cost_eps.csv.gz'), delimiter=",")
lapjv.lap(cost)
def test_lap_cost_limit():
ret = lapjv.lap(cost[:3, :3], cost_limit=4.99)
assert ret[0] == 3.0
assert np.all(ret[1] == [1, 2, -1])
assert np.all(ret[2] == [-1, 0, 1])
def test_lap_extension():
ret = lapjv.lap(cost[:2, :4], extend_cost=True)
assert ret[0] == 3.0
assert np.all(ret[1] == [1, 2])
assert np.all(ret[2] == [-1, 0, 1, -1])
def test_lap_non_contigous():
ret = lapjv.lap(cost[:3, :3])
assert ret[0] == 8.0
assert np.all(ret[1] == [1, 2, 0])
assert np.all(ret[2] == [2, 0, 1])
def test_lap_non_square_fail():
lapjv.lap(np.zeros((3, 2)))
def test_lap_empty():
lapjv.lap(np.ndarray([]))
