def test_netmf():
"""
Testing the NetMF class.
"""
model = NetMF()
graph = nx.watts_strogatz_graph(100, 10, 0.5)
model.fit(graph)
embedding = model.get_embedding()
assert embedding.shape[0] == graph.number_of_nodes()
assert embedding.shape[1] == model.dimensions
assert type(embedding) == np.ndarray
model = NetMF(dimensions=32)
graph = nx.watts_strogatz_graph(150, 10, 0.5)
model.fit(graph)
embedding = model.get_embedding()
assert embedding.shape[0] == graph.number_of_nodes()
assert embedding.shape[1] == model.dimensions
assert type(embedding) == np.ndarray
# Z = torch.from_numpy(Z)
print("Creating Graph Embedddings X and Z...\n")
G = nx.karate_club_graph()
A = nx.adjacency_matrix(G)
P = np.random.permutation(np.eye(A.shape[0], dtype=np.int))
_A = (P @ A) @ (np.linalg.inv(P))
_G = nx.from_numpy_matrix(_A)
correspondences = {}
for val in range(P.shape[0]):
correspondences[np.argmax(P[val, :])] = val
model = NetMF(dimensions=10)
_model = NetMF(dimensions=10)
model.fit(G)
_model.fit(_G)
X = model.get_embedding()
Z = model.get_embedding()
print("Graph Embeddings Created...\n")
X, Z = torch.from_numpy(X).double(), torch.from_numpy(Z).double()
N = X.shape[0]
BtB = torch.matmul(Z.T, Z)
DtD = torch.matmul(X.T, X)
normBtB = torch.norm(BtB.flatten())
normDtD = torch.norm(DtD.flatten())
manifold = DoublyStochastic(N)
regularizer = 0