def setUpClass(cls):
# 120 vertex graph w one community having 50 and another
# w 70 vertices
cls.n = [50, 70]
cls.vcount = np.cumsum(cls.n)
# define non-symmetric probability matrix as uneven
cls.Pns = np.vstack(([0.6, 0.2], [0.3, 0.4]))
# define symmetric probability as evenly weighted
cls.Psy = np.vstack(([0.6, 0.2], [0.3, 0.4]))
cls.Psy = symmetrize(cls.Psy)
cls.seed = 12345
def generate_data(n, seed=1, symetric=True):
"""Generate data form dirichelet distribution with
n numbers of points
"""
np.random.seed(seed)
parameters = [1, 1, 1]
# Generate latent positions
X = np.random.dirichlet(parameters, size=n)
# Generate probability matrix
P = np.dot(X, X.T)
# Generate two adjacencies
A1 = np.random.binomial(1, P)
A2 = np.random.binomial(1, P)
if symetric:
A1 = symmetrize(A1)
A2 = symmetrize(A2)
return (X, A1, A2)
def test_sbm(self):
n = [50, 60, 70]
vcount = np.cumsum(n)
# define symmetric probability as evenly weighted
Psy = np.vstack(([0.6, 0.2, 0.3], [0.3, 0.4, 0.2], [0.2, 0.8, 0.1]))
Psy = symmetrize(Psy)
np.random.seed(12345)
A = sbm(n, Psy)
for i in range(0, len(n)):
for j in range(0, len(n)):
irange = np.arange(vcount[i] - n[i], vcount[i])
jrange = np.arange(vcount[j] - n[j], vcount[j])
block = A[
(vcount[i] - n[i]) : vcount[i], (vcount[j] - n[j]) : vcount[j]
]
if i == j:
block = remove_diagonal(block)
self.assertTrue(np.isclose(np.mean(block), Psy[i, j], atol=0.02))
self.assertTrue(is_symmetric(A))
self.assertTrue(is_loopless(A))
# check dimensions
self.assertTrue(A.shape == (np.sum(n), np.sum(n)))
pass
def setUpClass(cls):
cls.n = [50, 70]
cls.Pns = np.vstack(([0.6, 0.2], [0.3, 0.4]))
# define symmetric probability as evenly weighted
cls.Psy = np.vstack(([0.6, 0.2], [0.3, 0.4]))
cls.Psy = symmetrize(cls.Psy)