def test_min_maximal_matching():
# smoke test
G = nx.Graph()
assert len(a.min_maximal_matching(G)) == 0
(1, 7), (1, 8), (2, 11), (2, 16), (2, 17), (3, 14), (3, 16), (3, 17), (4, 7), (4, 13), (4, 17), (5, 6), (5, 11), (6 ,18), (9 ,12), (10, 13), (11, 17), (13, 15), (15 ,17), (16 ,19)] graph=nx.Graph() graph.add_edges_from(graphEdges) print(graph.nodes()) print(graph.edges()) print(min_maximal_matching(graph)) print(graph) g1=nx.barabasi_albert_graph(1000,400) print(nx.maximal_independent_set((g1))) solution=[-1]*g1
def test_min_maximal_matching():
# smoke test
G = nx.Graph()
assert_equal(len(a.min_maximal_matching(G)), 0)
def create_graph(edgelist):
G = nx.DiGraph()
G.add_weighted_edges_from(edgelist)
# Graph layout
pos = nx.spring_layout(G)
# Remove nodes with out-degree = 1------------------------------------------
outdeg = nx.degree(G)
while 1 in outdeg.values():
to_remove = [n for n in outdeg if outdeg[n] == 1]
G.remove_nodes_from(to_remove)
# Remove edges also checking the two values of the tuple
edgelist = [x for x in edgelist if x[0] not in to_remove]
edgelist = [x for x in edgelist if x[1] not in to_remove]
outdeg = nx.degree(G)
# ---------------------------------------------------------------------------
# Node size ================================================================
d = nx.degree(G)
nx.draw_networkx_nodes(G, pos, nodelist=d.keys(), node_size=[v * 100 for v in d.values()])
# ===========================================================================
nx.draw_networkx_nodes(G, pos, cmap=plt.get_cmap("jet"), node_size=1)
edge_labels = dict([((u, v), d["weight"]) for u, v, d in G.edges(data=True)])
nx.draw_networkx_edges(G, pos, width=0.5, alpha=0.2)
nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels)
labels = {}
for node in G.nodes():
labels[node] = node
nx.draw_networkx_labels(G, pos, labels, font_size=8)
if not len(G) == 1:
from networkx.algorithms import approximation as apxm
print apxm.min_edge_dominating_set(G)
print apxm.max_clique(G)
print apxm.maximum_independent_set(G)
print apxm.min_maximal_matching(G)
print nx.degree_centrality(G)
plt.savefig("directed.png") # save as png
# clean old graph
plt.clf()
def test_min_maximal_matching():
# smoke test
G = nx.Graph()
assert_equal(len(a.min_maximal_matching(G)),0)
with_labels=False,
node_size=50,
nodelist=vl)
if el:
nx.draw_networkx_edges(g,
pos=pos,
ax=ax,
edge_color='r',
width=3,
edgelist=el)
ax.set_title(title)
ax.set_axis_off()
ax.set_aspect('equal')
if __name__ == '__main__':
g = gen()
fix_outer_cycle_pos(g, get_cycle(g, 3))
fix_all_pos(g)
vc = min_weighted_vertex_cover(g)
m = min_maximal_matching(g)
print m
fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(8, 4))
draw(g, ax1, 'vertex cover', vl=list(vc))
draw(g, ax2, 'matching', el=list(m))
plt.tight_layout()
plt.show()