def test_array_b(self):
b = np.zeros(10, dtype=int)
b[5:] = 20
expected = 1 - np.eye(10, dtype=int)
expected[:5] = 0
G = b_matching(self.dists, b)
assert_array_equal(G.matrix('dense').astype(int), expected)
def test_array_b(self):
b = np.zeros(10, dtype=int)
b[5:] = 20
expected = 1 - np.eye(10, dtype=int)
expected[:5] = 0
G = b_matching(self.dists, b)
assert_array_equal(G.matrix('dense').astype(int), expected)
def test_oscillation(self):
# Generated with the bdmatch binary (b=2,damp=1)
expected = np.array([[0, 0, 0, 1, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 1, 0],
[1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 1, 1, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0, 1],
[1, 0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 1, 0, 0, 0]])
G = b_matching(self.dists, 2, damping=1)
assert_array_equal(G.matrix('dense').astype(int), expected)
def test_standard(self):
# Generated with the bdmatch binary (b=2,damp=0.5)
expected = np.array([[0, 1, 0, 1, 0, 0, 0, 1, 0, 0],
[1, 0, 0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 1, 0, 1, 0],
[1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 1, 1, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 1, 0, 0, 0]]).T
G = b_matching(self.dists, 2, damping=0.5)
assert_array_equal(G.matrix('dense').astype(int), expected)
def test_oscillation(self):
# Generated with the bdmatch binary (b=2,damp=1)
expected = np.array([
[0, 0, 0, 1, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 1, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 1, 0],
[1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 1, 1, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0, 1],
[1, 0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 1, 0, 0, 0]])
G = b_matching(self.dists, 2, damping=1)
assert_array_equal(G.matrix('dense').astype(int), expected)
def test_standard(self):
# Generated with the bdmatch binary (b=2,damp=0.5)
expected = np.array([
[0, 1, 0, 1, 0, 0, 0, 1, 0, 0],
[1, 0, 0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 1, 0, 1, 0],
[1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 0, 0, 1, 1, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 1],
[0, 0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 1, 0, 0, 0]]).T
G = b_matching(self.dists, 2, damping=0.5)
assert_array_equal(G.matrix('dense').astype(int), expected)
def test_warn_nonconvergence(self):
with warnings.catch_warnings(record=True) as w:
b_matching(self.dists, 2, max_iter=2)
self.assertEqual(len(w), 1)
self.assertEqual(str(w[0].message),
'Hit iteration limit (2) before converging')
def test_warn_nonconvergence(self):
with warnings.catch_warnings(record=True) as w:
b_matching(self.dists, 2, max_iter=2)
self.assertEqual(len(w), 1)
self.assertEqual(str(w[0].message),
'Hit iteration limit (2) before converging')
