def test_all_inf():
cost = np.empty((5, 5), dtype=float)
cost[:] = np.inf
with raises(ValueError):
lapmod(*sparse_from_dense(cost))
with raises(ValueError):
lapmod(*sparse_from_masked(cost))
def test_inf_row():
cost = np.array(
[[0., 0., 0., 0., np.inf], [np.inf, np.inf, 0., 0., 0.],
[np.inf, np.inf, np.inf, np.inf, np.inf],
[np.inf, np.inf, np.inf, 0., 0.], [0., 0., 0., np.inf, np.inf]],
dtype=float)
with raises(ValueError):
ret = lapmod(*sparse_from_dense(cost))
ret = lapmod(*sparse_from_masked(cost))
assert len(ret) == 3
assert ret[0] == np.inf
def test_inf_unique():
cost = np.array([[1000, 4, 1], [1, 1000, 3], [5, 1, 1000]])
cost_ext = np.empty((4, 4))
cost_ext[:] = np.inf
cost_ext[:3, :3] = cost
cost_ext[3, 3] = 0
with raises(ValueError):
ret = lapmod(*sparse_from_dense(cost_ext))
ret = lapmod(*sparse_from_masked(cost_ext))
assert len(ret) == 3
assert ret[0] == 3.
assert np.all(ret[1] == [2, 0, 1, 3])
def test_dense_1kx1k_int_hard(dense_1kx1k_int_hard):
cost, opt = dense_1kx1k_int_hard
ret = lapmod(*sparse_from_dense(cost))
assert len(ret) == 3
assert ret[0] == opt
lapjv_ret = lapjv(cost)
assert ret[0] == lapjv_ret[0]
def test_dense_100x100_int(dense_100x100_int):
cost, opt = dense_100x100_int
ret = lapmod(*sparse_from_dense(cost))
assert len(ret) == 3
assert ret[0] == opt
lapjv_ret = lapjv(cost)
assert ret[0] == lapjv_ret[0]
def test_sparse_4kx4k_int(sparse_4kx4k_int):
cost, mask, opt = sparse_4kx4k_int
ret = lapmod(*sparse_from_masked(cost, mask))
assert len(ret) == 3
assert ret[0] == opt
cost[~mask] = get_platform_maxint()
lapjv_ret = lapjv(cost)
assert ret[0] == lapjv_ret[0]
def test_sparse_square(cost, expected):
ret = lapmod(*sparse_from_masked(cost))
assert len(ret) == len(expected)
assert cost[range(cost.shape[0]), ret[1]].sum() == ret[0]
assert cost[ret[2], range(cost.shape[1])].sum() == ret[0]
assert ret[0] == expected[0]
assert np.all(ret[1] == expected[1])
assert np.all(ret[2] == expected[2])
dense_ret = lapjv(cost)
assert ret[0] == dense_ret[0]
assert np.all(ret[1] == dense_ret[1])
assert np.all(ret[2] == dense_ret[2])
def test_infs_unsolvable():
cost = np.array(
[[0., 0., 0., np.inf, np.inf], [np.inf, np.inf, np.inf, 0., 0.],
[np.inf, np.inf, np.inf, 0., 0.], [np.inf, np.inf, np.inf, 0., 0.],
[0., 0., 0., np.inf, np.inf]],
dtype=float)
lapjv_ret = lapjv(cost)
assert lapjv_ret[0] == np.inf
ret = lapmod(*sparse_from_masked(cost))
assert len(ret) == 3
assert ret[0] == np.inf
cost = np.array(
[[19., 22., 16., np.inf, np.inf], [np.inf, np.inf, np.inf, 4., 13.],
[np.inf, np.inf, np.inf, 3., 14.], [np.inf, np.inf, np.inf, 10., 12.],
[11., 14., 13., np.inf, np.inf]],
dtype=float)
lapjv_ret = lapjv(cost)
assert lapjv_ret[0] == np.inf
ret = lapmod(*sparse_from_masked(cost))
assert len(ret) == 3
assert ret[0] == np.inf
def test_sparse_100x100_int(sparse_100x100_int):
cost, mask, opt = sparse_100x100_int
ret = lapmod(*sparse_from_masked(cost, mask))
assert len(ret) == 3
assert ret[0] == opt
def test_eps(dense_eps):
cost, opt = dense_eps
ret = lapmod(*sparse_from_dense(cost))
assert len(ret) == 3
assert ret[0] == opt