def test_ford_fulkerson(self, graph, f_max):
g = DirectedGraph().from_adjacency_matrix(graph)
f = ford_fulkerson(g)
gf_max = sum(weight for ((begin, end), weight) in f.items()
if begin == 1)
assert gf_max == f_max
def test_incidence_matrix(self):
g = DirectedGraph(8)
g.add_random_edges(15)
before = g.to_adjacency_list()
g.from_incidence_matrix(g.to_incidence_matrix())
after = g.to_adjacency_list()
assert before == after
def test_is_connected_graph(self):
g = DirectedGraph(3)
g.connect(1, 2)
g.connect(1, 3)
g.connect(2, 3)
assert not g.is_connected_graph()
g.connect(3, 1)
assert g.is_connected_graph()
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 example04():
g = DirectedGraph().from_adjacency_list(adjacency_list_2)
print(g)
rank = page_rank(g, algorithm="matrix")
display_pagerank(rank)
g.save("pr_matrix_2",
"png",
engine="dot",
color_components=True,
alphabetical=True)
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 test_components(self):
g = DirectedGraph(3)
g.connect(2, 1)
g.connect(1, 3)
g.connect(3, 1)
comps = g.components()
assert comps[1] == comps[3]
assert comps[2] != comps[1]
assert comps[2] != comps[3]
def test_component_list(self):
g = DirectedGraph(3)
g.connect(2, 1)
g.connect(1, 3)
g.connect(3, 1)
comps = g.component_list()
assert [1, 3] in comps.values()
assert [2] in comps.values()
assert [2, 1] not in comps.values()
def example01():
g = DirectedGraph().from_adjacency_list(adjacency_list_1)
print(g)
rank = page_rank(g, algorithm="random_walk")
display_pagerank(rank)
g.save(
"pr_random_walk_1",
"png",
engine="dot",
color_components=True,
alphabetical=True,
)
def get_random_flow_network(N: int) -> DirectedGraph:
"""
Losowa sieć przepływu
N - liczba warstw sieci
źródło - Node #1
ujście - Node #len(graph)
"""
assert N >= 2
print(f"liczba warstw: {N}")
# krok 1: tworzenie warstw
node_count_in_layer = [1] + [random.randint(2, N) for _ in range(N)] + [1]
node_layer = [None]
layer_nodes = []
total = 1
for i, count in enumerate(node_count_in_layer):
node_layer.extend([i] * count)
layer_nodes.append(list(range(total, total + count)))
total += count
print(f"liczba wierzchołków w warstwie: {node_count_in_layer}")
print(f"wierzchołki w warstwie: {layer_nodes}")
# krok 2: losowanie krawędzi między warstwami
g = DirectedGraph(size=sum(node_count_in_layer))
for i in range(1, N + 1):
for node in layer_nodes[i]:
# losowa krawędź wchodząca
g.connect(random.choice(layer_nodes[i - 1]), node)
# losowa krawędź wychodząca
g.connect(node, random.choice(layer_nodes[i + 1]))
# krok 3: odajemy 2N losowych łuków
edges_added = 0
while edges_added < 2 * N:
# brak krawędzi wychodzącej z ujścia
n1 = random.randint(1, len(g) - 1)
# brak krawędzi wchodzącej do źródła
n2 = random.randint(2, len(g))
if n1 == n2 or g.is_connected(n1, n2) or g.is_connected(n2, n1):
continue
g.connect(n1, n2)
edges_added += 1
# krok 4: przypisanie każdej krawędzi losowej przepustowości
g.assign_random_weights()
return g
def test_connect(self):
g = DirectedGraph(8)
n1 = 1
n2 = 2
g.connect(n1, n2)
assert g.is_connected(n1, n2)
assert not g.is_connected(n2, n1)
g.disconnect(n1, n2)
assert not g.is_connected(n1, n2)
g.connect(n1, n2)
g.connect(n2, n1)
assert g.is_connected(n1, n2)
assert g.is_connected(n2, n1)
def load_graph_to_work_on(args):
g = DirectedGraph()
if args.load:
g.load(args.load)
elif args.n:
g.add_nodes(int(args.n))
if args.l:
g.add_random_edges(int(args.l))
elif args.p:
g.connect_random(float(args.p))
if args.w is not None:
if args.w[0]:
g.assign_random_weights(int(args.w[0][0]), int(args.w[0][1]))
else:
g.assign_random_weights()
return g
def main():
print("Wywołanie:\n./lab05.py gen save_dir N\n./lab05.py ff save_dir")
print("Sieć przykładowa: ./lab05.py gen example\n")
if len(sys.argv) < 2:
return
if sys.argv[1] == "gen":
save_dir = sys.argv[2]
if save_dir == "example":
print(f"Używanie sieci przykładowej (z input_5.pdf)")
g = get_example()
else:
N = int(sys.argv[3])
print(f"Generowanie sieci przepływowej, N={N}")
g = generate_network(N=N)
prep_dir(save_dir)
g.save(save_dir + "/G", file_format="am")
g.save(save_dir + "/G", file_format="png", engine="dot")
print(f"Zapisano do katalogu {save_dir}")
elif sys.argv[1] == "ff":
save_dir = sys.argv[2]
g = DirectedGraph()
g.load(save_dir + "/G.am")
# odświeżenie diagramu w razie ręcznej edycji macierzy sąsiedztwa
g.save(save_dir + "/G", file_format="png", engine="dot")
print(f"Odczytano sieć przepływową z katalogu {save_dir}")
f = ford_fulkerson(g, verbose=True)
f_max = sum(weight for ((begin, end), weight) in f.items()
if begin == 1)
print(f"maksymalny przepływ: {f_max}")
labels = {(e.begin, e.end): f"{f[(e.begin, e.end)]}/{e.weight}"
for e in g.edges}
g.save(save_dir + "/G-ff",
file_format="png",
engine="dot",
edge_labels=labels)
def get_example():
exg = DirectedGraph(11)
s = 1
a = 2
b = 3
c = 4
d = 5
e = 6
f = 7
g = 8
h = 9
i = 10
t = 11
exg.connect(s, a, 10)
exg.connect(s, b, 3)
exg.connect(s, c, 6)
exg.connect(a, b, 8)
exg.connect(a, d, 8)
exg.connect(a, e, 6)
exg.connect(b, e, 2)
exg.connect(b, f, 10)
exg.connect(c, d, 9)
exg.connect(c, f, 1)
exg.connect(d, h, 5)
exg.connect(e, d, 1)
exg.connect(e, i, 7)
exg.connect(f, g, 9)
exg.connect(g, t, 7)
exg.connect(h, t, 5)
exg.connect(i, t, 7)
return exg
def get_random_digraph(max_size=20) -> DirectedGraph:
size = random.randint(2, max_size)
g = DirectedGraph(size)
g.connect_random(random.random())
return g