def test_mixed_nodetypes():
# Smoke test to make sure no TypeError is raised for mixed node types.
G = nx.Graph()
edgelist = [(0, 3, [('weight', 5)]), (0, '1', [('weight', 5)])]
G.add_edges_from(edgelist)
G = G.to_directed()
x = branchings.minimum_spanning_arborescence(G)
def test_mst():
# Make sure we get the same results for undirected graphs.
# Example from: http://en.wikipedia.org/wiki/Kruskal's_algorithm
G = nx.Graph()
edgelist = [(0, 3, [('weight', 5)]),
(0, 1, [('weight', 7)]),
(1, 3, [('weight', 9)]),
(1, 2, [('weight', 8)]),
(1, 4, [('weight', 7)]),
(3, 4, [('weight', 15)]),
(3, 5, [('weight', 6)]),
(2, 4, [('weight', 5)]),
(4, 5, [('weight', 8)]),
(4, 6, [('weight', 9)]),
(5, 6, [('weight', 11)])]
G.add_edges_from(edgelist)
G = G.to_directed()
x = branchings.minimum_spanning_arborescence(G)
edges = [(set([0, 1]), 7), (set([0, 3]), 5), (set([3, 5]), 6),
(set([1, 4]), 7), (set([4, 2]), 5), (set([4, 6]), 9)]
assert_equal(x.number_of_edges(), len(edges))
for u, v, d in x.edges(data=True):
assert_true( (set([u,v]), d['weight']) in edges )
def test_partition_spanning_arborescence():
"""
Test that we can generate minimum spanning arborescences which respect the
given partition.
"""
G = nx.from_numpy_array(G_array, create_using=nx.DiGraph)
G[3][0]["partition"] = nx.EdgePartition.EXCLUDED
G[2][3]["partition"] = nx.EdgePartition.INCLUDED
G[7][3]["partition"] = nx.EdgePartition.EXCLUDED
G[0][2]["partition"] = nx.EdgePartition.EXCLUDED
G[6][2]["partition"] = nx.EdgePartition.INCLUDED
actual_edges = [
(0, 4, 12),
(1, 0, 4),
(1, 5, 13),
(2, 3, 21),
(4, 7, 12),
(5, 6, 14),
(5, 8, 12),
(6, 2, 21),
]
B = branchings.minimum_spanning_arborescence(G, partition="partition")
assert_equal_branchings(build_branching(actual_edges), B)
def test_mst():
# Make sure we get the same results for undirected graphs.
# Example from: https://en.wikipedia.org/wiki/Kruskal's_algorithm
G = nx.Graph()
edgelist = [
(0, 3, [("weight", 5)]),
(0, 1, [("weight", 7)]),
(1, 3, [("weight", 9)]),
(1, 2, [("weight", 8)]),
(1, 4, [("weight", 7)]),
(3, 4, [("weight", 15)]),
(3, 5, [("weight", 6)]),
(2, 4, [("weight", 5)]),
(4, 5, [("weight", 8)]),
(4, 6, [("weight", 9)]),
(5, 6, [("weight", 11)]),
]
G.add_edges_from(edgelist)
G = G.to_directed()
x = branchings.minimum_spanning_arborescence(G)
edges = [
({0, 1}, 7),
({0, 3}, 5),
({3, 5}, 6),
({1, 4}, 7),
({4, 2}, 5),
({4, 6}, 9),
]
assert x.number_of_edges() == len(edges)
for u, v, d in x.edges(data=True):
assert ({u, v}, d["weight"]) in edges
def test_mst():
# Make sure we get the same results for undirected graphs.
# Example from: https://en.wikipedia.org/wiki/Kruskal's_algorithm
G = nx.Graph()
edgelist = [(0, 3, [('weight', 5)]),
(0, 1, [('weight', 7)]),
(1, 3, [('weight', 9)]),
(1, 2, [('weight', 8)]),
(1, 4, [('weight', 7)]),
(3, 4, [('weight', 15)]),
(3, 5, [('weight', 6)]),
(2, 4, [('weight', 5)]),
(4, 5, [('weight', 8)]),
(4, 6, [('weight', 9)]),
(5, 6, [('weight', 11)])]
G.add_edges_from(edgelist)
G = G.to_directed()
x = branchings.minimum_spanning_arborescence(G)
edges = [(set([0, 1]), 7), (set([0, 3]), 5), (set([3, 5]), 6),
(set([1, 4]), 7), (set([4, 2]), 5), (set([4, 6]), 9)]
assert_equal(x.number_of_edges(), len(edges))
for u, v, d in x.edges(data=True):
assert_true((set([u, v]), d['weight']) in edges)
def test_mixed_nodetypes():
# Smoke test to make sure no TypeError is raised for mixed node types.
G = nx.Graph()
edgelist = [(0, 3, [('weight', 5)]),
(0, '1', [('weight', 5)])]
G.add_edges_from(edgelist)
G = G.to_directed()
x = branchings.minimum_spanning_arborescence(G)
def test_edmonds2_minarbor():
G = G1()
x = branchings.minimum_spanning_arborescence(G)
# This was obtained from algorithm. Need to verify it independently.
# Branch weight is: 96
edges = [(3, 0, 5), (0, 2, 12), (0, 4, 12), (2, 5, 12), (4, 7, 12),
(5, 8, 12), (5, 6, 14), (2, 1, 17)]
x_ = build_branching(edges)
assert_equal_branchings(x, x_)
def test_edmonds2_minarbor():
G = G1()
x = branchings.minimum_spanning_arborescence(G)
# This was obtained from algorithm. Need to verify it independently.
# Branch weight is: 96
edges = [
(3, 0, 5), (0, 2, 12), (0, 4, 12), (2, 5, 12),
(4, 7, 12), (5, 8, 12), (5, 6, 14), (2, 1, 17)
]
x_ = build_branching(edges)
assert_equal_branchings(x, x_)
