def test_approx_coloring(self):
KR = kapoor_rizzi()
degree = 40
partition_n_nodes = 15
# generate random graph
g = graph_util().generate_random_regular_bipartite(partition_n_nodes, degree)
# generate arbitrary partitions for approximation algo
arbitrary = [graph_util().generate_random_regular_bipartite(partition_n_nodes, 1) for i in xrange((degree % 2) + 1)]
# algorithm is destructive so save these for later comparisons
original_nodes = g.nodes()
arbitrary_edges = reduce(lambda x,y: x+y, (m.edges() for m in arbitrary))
original_edges = g.edges() + arbitrary_edges
solution = KR.solve(degree, g, arbitrary)
# check the amount of matchings
self.assertEqual(len(solution), degree + len(arbitrary), "Didn't get enough matchings")
# check each matching:
for matching in solution:
# matching preserves nodes
self.assertEquals(matching.nodes(), original_nodes)
# every node has degree 1
self.assertEquals(nx.degree_histogram(matching), [0, 2*partition_n_nodes])
# matchings preserve edges
matching_edges = reduce(lambda x,y: x+y, (m.edges() for m in solution))
self.assertEquals(sorted(matching_edges), sorted(original_edges))#, "Mismatch between input and output edges")
def __init__(self, degree, n_nodes):
self.degree = degree
self.KR = kapoor_rizzi()
# generate random graph
self.g = graph_util().generate_random_regular_bipartite(n_nodes, degree)
# generate arbitrary partitions for approximation algo
self.arbitrary = [graph_util().generate_random_regular_bipartite(n_nodes, 1) for i in xrange((degree % 2) + 1)]
def __init__(self, degree, n_nodes):
self.degree = degree
self.KR = kapoor_rizzi()
# generate random graph
self.g = graph_util().generate_random_regular_bipartite(
n_nodes, degree)
# generate arbitrary partitions for approximation algo
self.arbitrary = [
graph_util().generate_random_regular_bipartite(n_nodes, 1)
for i in xrange((degree % 2) + 1)
]
def test_approx_coloring(self):
KR = kapoor_rizzi()
degree = 40
partition_n_nodes = 15
# generate random graph
g = graph_util().generate_random_regular_bipartite(
partition_n_nodes, degree)
# generate arbitrary partitions for approximation algo
arbitrary = [
graph_util().generate_random_regular_bipartite(
partition_n_nodes, 1) for i in xrange((degree % 2) + 1)
]
# algorithm is destructive so save these for later comparisons
original_nodes = g.nodes()
arbitrary_edges = reduce(lambda x, y: x + y,
(m.edges() for m in arbitrary))
original_edges = g.edges() + arbitrary_edges
solution = KR.solve(degree, g, arbitrary)
# check the amount of matchings
self.assertEqual(len(solution), degree + len(arbitrary),
"Didn't get enough matchings")
# check each matching:
for matching in solution:
# matching preserves nodes
self.assertEquals(matching.nodes(), original_nodes)
# every node has degree 1
self.assertEquals(nx.degree_histogram(matching),
[0, 2 * partition_n_nodes])
# matchings preserve edges
matching_edges = reduce(lambda x, y: x + y,
(m.edges() for m in solution))
self.assertEquals(sorted(matching_edges), sorted(
original_edges)) #, "Mismatch between input and output edges")
