def test_adjacency_matrix(self):
g = DirectedGraph(8)
g.add_random_edges(15)
before = g.to_adjacency_matrix()
g.from_adjacency_matrix(before)
after = g.to_adjacency_matrix()
assert before == after
def test_incidence_matrix_file(self):
g = DirectedGraph(8)
g.add_random_edges(15)
before = g.to_adjacency_matrix()
g.save("test", "im")
g.load("test.im")
after = g.to_adjacency_matrix()
assert before == after
def _page_rank_matrix(g: DirectedGraph, d) -> Dict[int, float]:
n = len(g.nodes)
P = np.zeros((n, n))
A = np.array(g.to_adjacency_matrix())
for i in range(n):
for j in range(n):
P[i][j] = (1.0 - d) * A[i][j] / g.node_degree(i + 1) + d / float(n)
p0 = np.full(n, 1 / n)
p1 = np.zeros(n)
i = 0
err = float("inf")
while err > 1e-11:
p1 = p0.dot(P)
diff = p1 - p0
err = sum(x**2 for x in diff)
p0 = p1
i += 1
print(f"Zbieżność uzyskano po {i} iteracjach.")
return {k: p1[k - 1] for k in range(1, n + 1)}