def test_bipartite_explicit(self):
G = nx.Graph()
G.add_nodes_from([1, 2, 3, 4], bipartite=0)
G.add_nodes_from(["a", "b", "c"], bipartite=1)
G.add_edges_from([(1, "a"), (1, "b"), (2, "b"), (2, "c"), (3, "c"), (4, "a")])
min_cover = nx.min_edge_cover(
G, nx.algorithms.bipartite.matching.eppstein_matching
)
min_cover2 = nx.min_edge_cover(G)
assert nx.is_edge_cover(G, min_cover)
assert len(min_cover) == 8
def test_bipartite_explicit(self):
G = nx.Graph()
G.add_nodes_from([1, 2, 3, 4], bipartite=0)
G.add_nodes_from(['a', 'b', 'c'], bipartite=1)
G.add_edges_from([(1, 'a'), (1, 'b'), (2, 'b'), (2, 'c'), (3, 'c'),
(4, 'a')])
min_cover = nx.min_edge_cover(
G, nx.algorithms.bipartite.matching.eppstein_matching)
min_cover2 = nx.min_edge_cover(G)
assert_true(nx.is_edge_cover(G, min_cover))
assert_equal(len(min_cover), 8)
def test_bipartite_explicit(self):
G = nx.Graph()
G.add_nodes_from([1, 2, 3, 4], bipartite=0)
G.add_nodes_from(['a', 'b', 'c'], bipartite=1)
G.add_edges_from([(1, 'a'), (1, 'b'), (2, 'b'),
(2, 'c'), (3, 'c'), (4, 'a')])
min_cover = nx.min_edge_cover(G, nx.algorithms.bipartite.matching.
eppstein_matching)
min_cover2 = nx.min_edge_cover(G)
assert_true(nx.is_edge_cover(G, min_cover))
assert_equal(len(min_cover), 8)
def min_edge_cover(edges):
g = create_graph(edges)
mc = nx.min_edge_cover(g)
res = set()
for i in mc:
res.add((min(i), max(i)))
return res
def compute_features(self):
self.add_feature(
"min_edge_cover",
lambda graph: len(list(nx.min_edge_cover(graph))),
"The number of edges which consistutes the minimum edge cover of the graph",
InterpretabilityScore(3),
)
def tst3():
edges = np.array([[1, 4], [2, 4], [3, 5], [1, 5]])
g = create_graph(edges)
min_cover = nx.min_edge_cover(g)
res = set()
for i in min_cover:
res.add((min(i), max(i)))
return res
def test_complete_graph(self):
G = nx.complete_graph(10)
min_cover = nx.min_edge_cover(G)
assert_true(nx.is_edge_cover(G, min_cover))
assert_equal(len(min_cover), 5)
def test_graph_single_edge(self):
G = nx.Graph()
G.add_edge(0, 1)
assert_equal(nx.min_edge_cover(G), {(0, 1)})
def test_graph_with_loop(self):
G = nx.Graph()
G.add_edge(0, 0)
assert_equal(nx.min_edge_cover(G), {(0, 0)})
def test_graph_with_loop(self):
G = nx.Graph()
G.add_edge(0, 0)
assert_equal(nx.min_edge_cover(G), {(0, 0)})
def test_complete_graph(self):
G = nx.complete_graph(10)
min_cover = nx.min_edge_cover(G)
assert nx.is_edge_cover(G, min_cover)
assert len(min_cover) == 5
def test_graph_single_edge(self):
G = nx.Graph()
G.add_edge(0, 1)
assert nx.min_edge_cover(G) in ({(0, 1)}, {(1, 0)})
ps = [int(i) for i in s.split()]
n = ps[0]
m = ps[1]
graph = nx.Graph()
for i in range(0, m):
s = f.readline()
ps = [float(i) for i in s.split()]
v = int(ps[0])
u = int(ps[1])
graph.add_edge(v, u, weight = 1)
f.close()
f = open("outputPy.txt", "w")
f.write("maximum stable set = " + str(approximation.independent_set.maximum_independent_set(graph)) + '\n')
f.write("maximum matching = " + str(nx.max_weight_matching(graph)) + '\n')
f.write("minimum vertex cover = " + str(approximation.vertex_cover.min_weighted_vertex_cover(graph)) + '\n')
f.write("minimum edge cover = " + str(nx.min_edge_cover(graph)))
f.write("minimum path that visits every edge: " + str(list(nx.eulerian_circuit(nx.eulerize(graph)))))
dists = dict(nx.all_pairs_shortest_path_length(graph))
paths = dict(nx.all_pairs_shortest_path(graph))
distsArr = []
listsCnt = 0
for i in range(1, n + 1):
if i == 9 or i == 27 or i == 19 or i == 46 or i == 18:
continue
distsArr.append([])
listsCnt += 1
for j in range(1, n + 1):
if j == 9 or j == 27 or j == 19 or j == 46 or j == 18:
continue
distsArr[listsCnt - 1].append(dists[i][j])
vertexes = []
def edge_cover(self):
graph = self.construct_network()
cover_edge = nx.min_edge_cover(graph)
return cover_edge
def min_edge_cover(graph):
"""min_edge_cover"""
return len(list(nx.min_edge_cover(graph)))
def test_empty_graph(self):
G = nx.Graph()
assert nx.min_edge_cover(G) == set()
def test_empty_graph(self):
G = nx.Graph()
assert_equal(nx.min_edge_cover(G), set())
def test_graph_with_loop(self):
G = nx.Graph()
G.add_edge(0, 0)
assert nx.min_edge_cover(G) == {(0, 0)}
def test_graph_single_edge(self):
G = nx.Graph()
G.add_edge(0, 1)
assert_equal(nx.min_edge_cover(G),
{(0, 1)})
def test_empty_graph(self):
G = nx.Graph()
assert_equal(nx.min_edge_cover(G), set())
def test_complete_graph(self):
G = nx.complete_graph(10)
min_cover = nx.min_edge_cover(G)
assert_true(nx.is_edge_cover(G, min_cover))
assert_equal(len(min_cover), 5)
import util import networkx as nx from networkx.algorithms import approximation from networkx.algorithms import connectivity state_ = 15 input_ = 4 output_ = 1 file_name = 'test.kiss2' repeated = 5 experiment = 10 util.create_fsm(state_, input_, output_, file_name) G, g_data = util.create_graph(file_name) print(nx.average_neighbor_degree(G)) print(approximation.max_clique(G)) print(nx.k_nearest_neighbors(G)) print(nx.min_edge_cover(G))
