def test_constrained_cov_0():
n = 10
for _ in range(10):
g = Graph(n)
g.SetRandom()
a = g.GetAdjM()[np.triu_indices(n, 1)]
T = np.random.random((n,n))
C = T.transpose() @ T
C_star = constrained_cov(g.GetDOL(), C, np.eye(n))
K = np.linalg.inv(C_star)
b = (abs(K[np.triu_indices(n, 1)]) > 1e-10).astype(int)
assert((a == b).all())
def test_generate_data():
np.random.seed(123)
for _ in range(10):
n = 10
m = 10000
g = Graph(n)
g.SetRandom()
X = generate_data(n, m, g, threshold=1e-6)
a = g.GetAdjM()[np.triu_indices(n, 1)]
b = (np.abs(np.linalg.inv((X.transpose() @ X) / (m-1))[np.triu_indices(n, 1)]) > 0.1).astype(int)
threshold = n
assert(np.sum(a != b) < threshold)
# Larger matrices require very large number of samples for it to converge to the correct graph
