def test_one_exchange_basic():
G = nx.complete_graph(5)
random.seed(5)
for (u, v, w) in G.edges(data=True):
w["weight"] = random.randrange(-100, 100, 1) / 10
initial_cut = set(random.sample(G.nodes(), k=5))
cut_size, (set1, set2) = maxcut.one_exchange(G,
initial_cut,
weight="weight",
seed=5)
_is_valid_cut(G, set1, set2)
_cut_is_locally_optimal(G, cut_size, set1)
def test_one_exchange_optimal():
# Greedy one exchange should find the optimal solution for this graph (14)
G = nx.Graph()
G.add_edge(1, 2, weight=3)
G.add_edge(1, 3, weight=3)
G.add_edge(1, 4, weight=3)
G.add_edge(1, 5, weight=3)
G.add_edge(2, 3, weight=5)
cut_size, (set1, set2) = maxcut.one_exchange(G, weight="weight", seed=5)
_is_valid_cut(G, set1, set2)
_cut_is_locally_optimal(G, cut_size, set1)
# check global optimality
assert cut_size == 14
def test_negative_weights():
G = nx.complete_graph(5)
random.seed(5)
for (u, v, w) in G.edges(data=True):
w["weight"] = -1 * random.random()
initial_cut = set(random.sample(G.nodes(), k=5))
cut_size, (set1, set2) = maxcut.one_exchange(G,
initial_cut,
weight="weight")
# make sure it is a valid cut
_is_valid_cut(G, set1, set2)
# check local optimality
_cut_is_locally_optimal(G, cut_size, set1)
# test that all nodes are in the same partition
assert len(set1) == len(G.nodes) or len(set2) == len(G.nodes)