def test_source_sink_scale_free(n):
seed_number = randint(1, 1000)
generator = ScaleFree(n, directed=True, seed_number=seed_number)
graph, source, sink = generator.generate()
count_source, src = has_unique_source(graph)
count_sink, snk = has_unique_sink(graph)
assert count_source == 1
assert count_sink == 1
assert source == src
assert sink == snk
def get_graph(graph_type, n, m, directed, seed_number):
if graph_type == 'random':
graph = Random(n, m, seed_number=seed_number, directed=directed)
elif graph_type == 'scale-free':
graph = ScaleFree(n, seed_number=seed_number, directed=directed)
else:
graph = Triangulation(n,
type=graph_type,
seed_number=seed_number,
directed=directed)
return graph
def test_max_flow_scale_free_directed(n):
seed_number = randint(1, 1000)
generator = ScaleFree(n, directed=True, seed_number=seed_number)
graph, source, target = generator.generate()
solver = GoldbergWave(graph)
max_flow = solver.get_max_flow(source, target)
generator = ScaleFree(n, directed=True, seed_number=seed_number)
graph, source, target = generator.generate()
res = gt.push_relabel_max_flow(graph, source, target, graph.ep.cap)
res.a = graph.ep.cap.a - res.a # the actual flow
gt_max_flow = sum(res[e] for e in target.in_edges())
assert max_flow == gt_max_flow