class TestSpanningTrees(unittest.TestCase):
def setUp(self):
self.graph = Graph()
self.graph.add_edges([('a', 'b'), ('a', 's'), ('b', 'c'), ('c', 's'),
('d', 'e'), ('d', 's'), ('e', 'd'), ('e', 's')])
def test_breadth_first_search(self):
vertices = list(breadth_first_search(self.graph, 's'))
self.assertEqual(vertices, ['s', 'a', 'c', 'e', 'd', 'b'])
vertices = list(breadth_first_search(self.graph, 's', 1))
self.assertEqual(vertices, ['s', 'a', 'c', 'e', 'd'])
def test_breadth_first_search_tree(self):
pred = breadth_first_search_tree(self.graph, 's')
ans = {'a': 's', 'b': 'a', 'c': 's', 'd': 's', 'e': 's', 's': None}
self.assertEqual(pred, ans)
def test_depth_first_search(self):
vertices = list(depth_first_search(self.graph, 's'))
self.assertEqual(vertices, ['s', 'd', 'e', 'c', 'b', 'a'])
vertices = list(depth_first_search(self.graph, 's', 1))
self.assertEqual(vertices, ['s', 'd', 'e', 'c', 'a'])
def test_depth_first_search_tree(self):
pred = depth_first_search_tree(self.graph, 's')
ans = {'a': 'b', 'b': 'c', 'c': 's', 'd': 's', 'e': 'd', 's': None}
self.assertEqual(pred, ans)
class TestSpanningTrees(unittest.TestCase):
def setUp(self):
self.graph = Graph()
self.graph.add_edges([
('a', 'b'), ('a', 's'), ('b', 'c'), ('c', 's'),
('d', 'e'), ('d', 's'), ('e', 'd'), ('e', 's')
])
def test_breadth_first_search(self):
vertices = list(breadth_first_search(self.graph, 's'))
self.assertEqual(vertices, ['s', 'a', 'c', 'e', 'd', 'b'])
vertices = list(breadth_first_search(self.graph, 's', 1))
self.assertEqual(vertices, ['s', 'a', 'c', 'e', 'd'])
def test_breadth_first_search_tree(self):
pred = breadth_first_search_tree(self.graph, 's')
ans = {'a': 's', 'b': 'a', 'c': 's', 'd': 's', 'e': 's', 's': None}
self.assertEqual(pred, ans)
def test_depth_first_search(self):
vertices = list(depth_first_search(self.graph, 's'))
self.assertEqual(vertices, ['s', 'd', 'e', 'c', 'b', 'a'])
vertices = list(depth_first_search(self.graph, 's', 1))
self.assertEqual(vertices, ['s', 'd', 'e', 'c', 'a'])
def test_depth_first_search_tree(self):
pred = depth_first_search_tree(self.graph, 's')
ans = {'a': 'b', 'b': 'c', 'c': 's', 'd': 's', 'e': 'd', 's': None}
self.assertEqual(pred, ans)
class TestSpanningTrees(unittest.TestCase):
def setUp(self):
# Graph in Section 3.2.2 of Dasgupta
self.graph = Graph()
self.graph.add_vertices(['a', 'b', 'c', 'd', 'e', 'f',
'g', 'h', 'i', 'j', 'k', 'l'])
self.graph.add_edges([('a', 'b'), ('a', 'e'), ('e', 'i'), ('e', 'j'),
('i', 'j'), ('c', 'd'), ('c', 'g'), ('c', 'h'),
('d', 'h'), ('g', 'h'), ('g', 'k'), ('h', 'k'),
('h', 'l')])
self.graph_components = [
['a', 'b', 'e', 'i', 'j'],
['c', 'h', 'd', 'g', 'k', 'l'],
['f']
]
self.graph_component = ['a', 'b', 'e', 'i', 'j']
def test_connected_component(self):
component = connected_component(self.graph, 'a')
self.assertEqual(component, self.graph_component)
def test_connected_components(self):
components = connected_components(self.graph)
self.assertEqual(components, self.graph_components)
def setUp(self):
graph = Graph()
graph.add_edges([('a', 'b', 2), ('a', 'c', 1), ('b', 'c', 1),
('b', 'd', 2), ('b', 'e', 3), ('c', 'e', 4),
('d', 'e', 2)])
self.graph = graph
self.distance = {'a': 0, 'b': 2, 'c': 1, 'd': 4, 'e': 5}
self.previous = {'a': None, 'b': 'a', 'c': 'a', 'd': 'b', 'e': 'c'}
def setUp(self):
# Example in Section 5.1.5 in Dasgupta
graph = Graph()
graph.add_edges([('a', 'b', 5), ('a', 'c', 6), ('a', 'd', 4),
('b', 'c', 1), ('b', 'd', 2), ('c', 'd', 2),
('c', 'e', 5), ('c', 'f', 3), ('d', 'f', 4),
('e', 'f', 4)])
self.dasgupta_graph = graph
self.dasgupta_weight = 14
self.dasgupta_edges = [('a', 'd'), ('b', 'c'), ('b', 'd'), ('c', 'f'),
('e', 'f')]
self.dasgupta_dict = {
'a': None,
'b': 'd',
'c': 'b',
'd': 'a',
'e': 'f',
'f': 'c'
}
graph = Graph()
graph.add_edges([(1, 2, 3), (1, 3, 4), (1, 4, 5), (2, 3, 4),
(3, 4, 5)])
self.gabow_graph = graph
self.gabow_trees = [(12, [(1, 2), (1, 3), (1, 4)]),
(12, [(1, 2), (1, 4), (2, 3)]),
(12, [(1, 2), (1, 3), (3, 4)]),
(12, [(1, 2), (2, 3), (3, 4)]),
(13, [(1, 3), (1, 4), (2, 3)]),
(13, [(1, 3), (2, 3), (3, 4)]),
(13, [(1, 2), (1, 4), (3, 4)]),
(14, [(1, 4), (2, 3), (3, 4)])]
self.petersen_graph = petersen_graph()
def setUp(self):
# Graph in Section 3.2.2 of Dasgupta
self.graph = Graph()
self.graph.add_vertices(
['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l'])
self.graph.add_edges([('a', 'b'), ('a', 'e'), ('e', 'i'), ('e', 'j'),
('i', 'j'), ('c', 'd'), ('c', 'g'), ('c', 'h'),
('d', 'h'), ('g', 'h'), ('g', 'k'), ('h', 'k'),
('h', 'l')])
self.graph_components = [['a', 'b', 'e', 'i', 'j'],
['c', 'h', 'd', 'g', 'k', 'l'], ['f']]
self.graph_component = ['a', 'b', 'e', 'i', 'j']
def setUp(self):
self.petersen_graph = petersen_graph()
self.random_graph10_5 = random_graph(10, 0.5)
self.random_graph100_3 = random_graph(100, 0.3)
self.petersen_cliques = [
[0, 1], [0, 4], [0, 5], [1, 2], [1, 6],
[2, 3], [2, 7], [3, 4], [3, 8], [4, 9],
[5, 7], [5, 8], [6, 8], [6, 9], [7, 9],
]
self.random_graph_edges = [
(0, 2), (0, 3), (0, 5), (0, 7),
(0, 9), (1, 2), (1, 3), (1, 5),
(1, 6), (1, 7), (1, 9), (2, 3),
(2, 5), (2, 6), (2, 7), (2, 8),
(3, 5), (3, 7), (4, 6), (4, 7),
(5, 6), (5, 7), (5, 9), (6, 7),
(6, 8), (6, 9), (7, 9),
]
self.random_graph = Graph()
self.random_graph.add_edges(self.random_graph_edges)
self.random_graph_clique = [0, 2, 3, 5, 7]
self.coprime_graph10 = coprime_pairs_graph(10)
self.coprime_graph10_weight = 30
self.coprime_graph10_clique = [1, 5, 7, 8, 9]
self.coprime_graph100 = coprime_pairs_graph(100)
self.coprime_graph100_weight = 1356
self.coprime_graph100_clique = [
1, 17, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61,
67, 71, 73, 79, 81, 83, 88, 89, 91, 95, 97,
]
def setUp(self):
# Example in Section 5.1.5 in Dasgupta
graph = Graph()
graph.add_edges([('u1', 'u2', 1), ('u1', 'v1', 2), ('u1', 'v2', 2),
('u2', 'v3', 4), ('u2', 'v4', 3), ('u2', 'v5', 5),
('u3', 'v1', 2), ('u3', 'v3', 8), ('u4', 'v2', 8),
('u4', 'v5', 8), ('v1', 'v2', 8), ('v1', 'v3', 9),
('v2', 'v5', 5), ('v3', 'v4', 8)])
self.graph1 = graph
self.terminals1 = ['v1', 'v2', 'v3', 'v4', 'v5']
self.weight1 = 17
self.edges1 = [('u1', 'u2'), ('u1', 'v1'), ('u1', 'v2'), ('u2', 'v3'), ('u2', 'v4'), ('u2', 'v5')]
graph = Graph()
graph.add_edges([(0, 1, 1), (0, 4, 2), (1, 2, 3), (1, 4, 1), (1, 5, 2),
(2, 3, 2), (2, 6, 6), (3, 6, 3), (3, 7, 1), (4, 5, 3),
(4, 8, 3), (4, 9, 4), (5, 6, 1), (5, 9, 4), (5, 10, 3),
(6, 7, 4), (6, 10, 2), (7, 10, 1), (7, 11, 2), (8, 9, 2),
(8, 12, 1), (9, 10, 5), (9, 13, 6), (10, 11, 3), (10, 13, 1),
(10, 14, 1), (10, 15, 4), (11, 15, 1), (12, 13, 3),
(13, 14, 4), (14, 15, 6)])
self.graph2 = graph
self.terminals2 = [0, 3, 6, 9, 12, 15]
self.weight2 = 18
self.edges2 = [(0, 1), (1, 5), (3, 6), (3, 7), (5, 6), (5, 9), (7, 11), (8, 9), (8, 12), (11, 15)]
graph = Graph()
edges = [
(0, 10, 0.18711581985575576), (1, 9, 0.8355759080627565), (1, 16, 0.7333895522013495),
(2, 8, 0.2179642234669944), (2, 13, 0.8912036215549415), (2, 19, 0.6431373258117827),
(2, 24, 0.04038712999015037), (3, 6, 0.12324233297257259), (4, 6, 0.8716054380512974),
(4, 14, 0.8394000635595052), (5, 13, 0.5808432084428543), (5, 15, 0.3817889531355405),
(6, 28, 0.7104386125476283), (7, 17, 0.8196386513589949), (7, 22, 0.5415599110939858),
(7, 26, 0.48022044306517697), (10, 13, 0.2289107839123704), (10, 27, 0.6078612772657713),
(10, 28, 0.18320583236837396), (11, 14, 0.21670116683074658), (11, 23, 0.814622874490316),
(11, 24, 0.2741012085352842), (12, 14, 0.020838710732781984), (12, 26, 0.32305190820208796),
(13, 16, 0.7798216089004076), (14, 24, 0.42823907465971456), (14, 29, 0.8005872492974788),
(15, 24, 0.5714901649169496), (16, 23, 0.3254825323952686), (17, 19, 0.4371620863350263),
(17, 20, 0.7305916551727157), (17, 26, 0.20057807143450757), (18, 25, 0.99192719132646),
(19, 25, 0.4630494211413302), (20, 29, 0.02443067673210797), (21, 22, 0.19217126655249206),
(21, 29, 0.5188924055605231), (22, 25, 0.7632549163886935), (24, 28, 0.8954891075360596)
]
graph.add_edges(edges)
self.graph3 = graph
self.terminals3 = range(5)
self.weight3 = 4.708933602364698
self.edges3 = [(0, 10), (1, 16), (2, 13), (3, 6), (4, 6), (6, 28), (10, 13), (10, 28), (13, 16)]
def setUp(self):
# Example in Section 5.1.5 in Dasgupta
graph = Graph()
graph.add_edges([('a', 'b', 5), ('a', 'c', 6), ('a', 'd', 4),
('b', 'c', 1), ('b', 'd', 2), ('c', 'd', 2),
('c', 'e', 5), ('c', 'f', 3), ('d', 'f', 4),
('e', 'f', 4)])
self.dasgupta_graph = graph
self.dasgupta_weight = 14
self.dasgupta_edges = [('a', 'd'), ('b', 'c'), ('b', 'd'), ('c', 'f'), ('e', 'f')]
self.dasgupta_dict = {'a': None, 'b': 'd', 'c': 'b', 'd': 'a', 'e': 'f', 'f': 'c'}
graph = Graph()
graph.add_edges([(1, 2, 3), (1, 3, 4), (1, 4, 5), (2, 3, 4), (3, 4, 5)])
self.gabow_graph = graph
self.gabow_trees = [
(12, [(1, 2), (1, 3), (1, 4)]),
(12, [(1, 2), (1, 4), (2, 3)]),
(12, [(1, 2), (1, 3), (3, 4)]),
(12, [(1, 2), (2, 3), (3, 4)]),
(13, [(1, 3), (1, 4), (2, 3)]),
(13, [(1, 3), (2, 3), (3, 4)]),
(13, [(1, 2), (1, 4), (3, 4)]),
(14, [(1, 4), (2, 3), (3, 4)])
]
self.petersen_graph = petersen_graph()
class TestSpanningTrees(unittest.TestCase):
def setUp(self):
# Graph in Section 3.2.2 of Dasgupta
self.graph = Graph()
self.graph.add_vertices(
['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l'])
self.graph.add_edges([('a', 'b'), ('a', 'e'), ('e', 'i'), ('e', 'j'),
('i', 'j'), ('c', 'd'), ('c', 'g'), ('c', 'h'),
('d', 'h'), ('g', 'h'), ('g', 'k'), ('h', 'k'),
('h', 'l')])
self.graph_components = [['a', 'b', 'e', 'i', 'j'],
['c', 'h', 'd', 'g', 'k', 'l'], ['f']]
self.graph_component = ['a', 'b', 'e', 'i', 'j']
def test_connected_component(self):
component = connected_component(self.graph, 'a')
self.assertEqual(component, self.graph_component)
def test_connected_components(self):
components = connected_components(self.graph)
self.assertEqual(components, self.graph_components)
def _delete_nonterminal_leaves(edges, terminals):
r"""Prunes all non-terminal leaves from the tree.
Given the edges of a tree and a set of terminal vertices, this method
removes all non-terminal leaves and returns the new tree edges.
Parameters
----------
edges : list
A list of the edges of the tree.
terminals : set
A set of the terminal vertices.
Returns
-------
tree_edges : list
A list of the edges of the pruned tree.
Notes
-----
This method deletes all non-terminal leaves from the tree. This process
is repeated until all leaves are terminals. There is room to improve
this algorithm.
"""
# Form a graph from the edge list.
tree = Graph()
for (u, v) in edges:
tree.add_edge(u, v)
# Now delete all leaves that are not terminals
while True:
leaves = tree.leaves()
changed = False
for leaf in leaves:
if leaf not in terminals:
tree.delete_vertex(leaf)
changed = True
if not changed:
break
return tree.edges()
def setUp(self):
# Graph in Section 3.2.2 of Dasgupta
self.graph = Graph()
self.graph.add_vertices(['a', 'b', 'c', 'd', 'e', 'f',
'g', 'h', 'i', 'j', 'k', 'l'])
self.graph.add_edges([('a', 'b'), ('a', 'e'), ('e', 'i'), ('e', 'j'),
('i', 'j'), ('c', 'd'), ('c', 'g'), ('c', 'h'),
('d', 'h'), ('g', 'h'), ('g', 'k'), ('h', 'k'),
('h', 'l')])
self.graph_components = [
['a', 'b', 'e', 'i', 'j'],
['c', 'h', 'd', 'g', 'k', 'l'],
['f']
]
self.graph_component = ['a', 'b', 'e', 'i', 'j']
def setUp(self):
graph = Graph()
graph.add_edges([('a', 'b', 2), ('a', 'c', 1), ('b', 'c', 1),
('b', 'd', 2), ('b', 'e', 3), ('c', 'e', 4),
('d', 'e', 2)])
self.graph = graph
self.distance = {'a': 0, 'b': 2, 'c': 1, 'd': 4, 'e': 5}
self.previous = {'a': None, 'b': 'a', 'c': 'a', 'd': 'b', 'e': 'c'}
graph = Graph()
edges = [('a', 'd', 4), ('a', 'e', 1), ('a', 'h', 10), ('b', 'c', 2), ('b', 'f', 1), ('c', 'f', 3),
('d', 'h', 1), ('e', 'f', 3), ('e', 'h', 5), ('f', 'g', 7), ('f', 'i', 1), ('g', 'j', 1),
('h', 'i', 9), ('i', 'j', 2)]
graph.add_edges(edges)
self.graph2 = graph
self.distance2 = {'a': 4, 'c': 11, 'b': 9, 'e': 5, 'd': 0, 'g': 12, 'f': 8, 'i': 9, 'h': 1, 'j': 11}
self.previous2 = {'a': 'd', 'c': 'f', 'b': 'f', 'e': 'a', 'd': None,
'g': 'j', 'f': 'e', 'i': 'f', 'h': 'd', 'j': 'i'}
def setUp(self):
self.graph = Graph()
self.graph.add_edges([('a', 'b'), ('a', 's'), ('b', 'c'), ('c', 's'),
('d', 'e'), ('d', 's'), ('e', 'd'), ('e', 's')])
def distance_network_heuristic(graph, terminals):
r"""Returns an approximate minimal Steiner tree connecting `terminals`
in `graph`.
Given a connected, undirected graph `graph` with positive edge weights
and a subset of the vertices `terminals`, this method finds a subgraph
with near-minimal total edge cost among all connected subgraphs that
contain these vertices.
Parameters
----------
graph : rosemary.graphs.graphs.Graph
terminals : list or set or tuple
A collection of vertices of `graph`.
Returns
-------
(weight, edges) : tuple
The number `weight` is the weight of the Steiner tree. The list
`edges` is a list of the edges in the Steiner tree.
Notes
-----
The Steiner problem in graphs asks to find the minimal tree connecting
the terminal vertices. This problem is NP-complete and has proven to be
extremely difficult even for small problem instances. Given this, it is
of practical importance to have heuristics for finding low-cost Steiner
trees.
This method uses the heuristic algorithm given in [1], which produces a
tree spanning the terminals with total cost <= 2*(1 - 1/t)*MST, where t
is the number of terminal vertices and MST is the weight of a minimal
Steiner tree. We also apply the improvement procedure given in [3].
The implementation given runs in time O(t*|E|*log(|V|)), where |E| is
the number of edges of `graph` and |V| is the number of vertices.
References
----------
.. [1] L. Kou, G. Markowsky, L. Berman, "A Fast Algorithm for Steiner
Trees", Acta Informatica, Volume 15, Issue 2, June 1981, 141-145.
.. [2] P. Winter, "Steiner Problem in Networks: A Survey", Networks,
Volume 17, Issue 2, Summer 1987, 129-167.
.. [3] S. Voss, "Steiner's Problem in Graphs: Heuristic Methods",
Discrete Applied Mathematics 40, 1992, 45-72.
.. [4] H.J. Proemel, A. Steger, "The Steiner Tree Problem - A Tour
through Graphs, Algorithms, and Complexity", Vieweg, 2002.
Examples
--------
>>> G = Graph()
>>> G.add_edges([('u1', 'u2', 1), ('u1', 'v1', 2), ('u1', 'v2', 2),
('u2', 'v3', 4), ('u2', 'v4', 3), ('u2', 'v5', 5),
('u3', 'v1', 2), ('u3', 'v3', 8), ('u4', 'v2', 8),
('u4', 'v5', 8), ('v1', 'v2', 8), ('v1', 'v3', 9),
('v2', 'v5', 5), ('v3', 'v4', 8)])
>>> distance_network_heuristic(G, ['v1', 'v2', 'v3', 'v4', 'v5'])
(17, [('u1', 'u2'), ('u1', 'v1'), ('u1', 'v2'), ('u2', 'v3'),
('u2', 'v4'), ('v2', 'v5')])
"""
# Create the distance network induced by the terminal set.
distance_network = Graph()
# shortest_prev[u] holds the predecessor dict for the shortest path
# tree rooted at u.
shortest_prev = {}
shortest_dist = {}
for u in terminals:
u_dist, u_prev = dijkstra(graph, u)
shortest_dist[u] = u_dist
shortest_prev[u] = u_prev
# For each pair (u, v) of terminal vertices, add an edge with weight
# equal to the length of the shortest u, v path.
distance_network.add_edges([(u, v, shortest_dist[u][v]) for (u, v) in itertools.combinations(terminals, 2)])
# Determine the minimum spanning tree of the distance network.
_, mst_edges = prim(distance_network, edge_list=True)
subnetwork = Graph()
# Construct a subnetwork of the graph by replacing each edge in the
# minimum spanning tree by the corresponding minimum cost path.
for (u, v) in mst_edges:
a, b = shortest_prev[u][v], v
while a is not None:
subnetwork.add_edge(a, b, graph[a][b])
a, b = shortest_prev[u][a], a
# Determine the minimum spanning tree of the subnetwork.
_, subnetwork_mst_edges = prim(subnetwork, edge_list=True)
tree_weight, tree_edges = _improve(graph, subnetwork_mst_edges, terminals)
return (tree_weight, tree_edges)
def setUp(self):
# Example in Section 5.1.5 in Dasgupta
graph = Graph()
graph.add_edges([('u1', 'u2', 1), ('u1', 'v1', 2), ('u1', 'v2', 2),
('u2', 'v3', 4), ('u2', 'v4', 3), ('u2', 'v5', 5),
('u3', 'v1', 2), ('u3', 'v3', 8), ('u4', 'v2', 8),
('u4', 'v5', 8), ('v1', 'v2', 8), ('v1', 'v3', 9),
('v2', 'v5', 5), ('v3', 'v4', 8)])
self.graph1 = graph
self.terminals1 = ['v1', 'v2', 'v3', 'v4', 'v5']
self.weight1 = 17
self.edges1 = [('u1', 'u2'), ('u1', 'v1'), ('u1', 'v2'), ('u2', 'v3'),
('u2', 'v4'), ('u2', 'v5')]
graph = Graph()
graph.add_edges([(0, 1, 1), (0, 4, 2), (1, 2, 3), (1, 4, 1), (1, 5, 2),
(2, 3, 2), (2, 6, 6), (3, 6, 3), (3, 7, 1), (4, 5, 3),
(4, 8, 3), (4, 9, 4), (5, 6, 1), (5, 9, 4),
(5, 10, 3), (6, 7, 4), (6, 10, 2), (7, 10, 1),
(7, 11, 2), (8, 9, 2), (8, 12, 1), (9, 10, 5),
(9, 13, 6), (10, 11, 3), (10, 13, 1), (10, 14, 1),
(10, 15, 4), (11, 15, 1), (12, 13, 3), (13, 14, 4),
(14, 15, 6)])
self.graph2 = graph
self.terminals2 = [0, 3, 6, 9, 12, 15]
self.weight2 = 18
self.edges2 = [(0, 1), (1, 5), (3, 6), (3, 7), (5, 6), (5, 9), (7, 11),
(8, 9), (8, 12), (11, 15)]
graph = Graph()
edges = [(0, 10, 0.18711581985575576), (1, 9, 0.8355759080627565),
(1, 16, 0.7333895522013495), (2, 8, 0.2179642234669944),
(2, 13, 0.8912036215549415), (2, 19, 0.6431373258117827),
(2, 24, 0.04038712999015037), (3, 6, 0.12324233297257259),
(4, 6, 0.8716054380512974), (4, 14, 0.8394000635595052),
(5, 13, 0.5808432084428543), (5, 15, 0.3817889531355405),
(6, 28, 0.7104386125476283), (7, 17, 0.8196386513589949),
(7, 22, 0.5415599110939858), (7, 26, 0.48022044306517697),
(10, 13, 0.2289107839123704), (10, 27, 0.6078612772657713),
(10, 28, 0.18320583236837396), (11, 14, 0.21670116683074658),
(11, 23, 0.814622874490316), (11, 24, 0.2741012085352842),
(12, 14, 0.020838710732781984), (12, 26, 0.32305190820208796),
(13, 16, 0.7798216089004076), (14, 24, 0.42823907465971456),
(14, 29, 0.8005872492974788), (15, 24, 0.5714901649169496),
(16, 23, 0.3254825323952686), (17, 19, 0.4371620863350263),
(17, 20, 0.7305916551727157), (17, 26, 0.20057807143450757),
(18, 25, 0.99192719132646), (19, 25, 0.4630494211413302),
(20, 29, 0.02443067673210797), (21, 22, 0.19217126655249206),
(21, 29, 0.5188924055605231), (22, 25, 0.7632549163886935),
(24, 28, 0.8954891075360596)]
graph.add_edges(edges)
self.graph3 = graph
self.terminals3 = range(5)
self.weight3 = 4.708933602364698
self.edges3 = [(0, 10), (1, 16), (2, 13), (3, 6), (4, 6), (6, 28),
(10, 13), (10, 28), (13, 16)]
def gabow(graph, k=None):
def find_exchange(father, included, excluded):
X = NamedUnionFind(vertices)
find = X.find
union = X.union
(min_weight, e, f) = (inf, None, None)
for (x, y) in included:
# Make it so that y = father[x]
if y != father[x]:
x, y = y, x
y = find(y)
union(x, y, y)
for edge in excluded:
mark(edge)
for (x, y) in edges:
if (x, y) in marked or (y, x) in marked:
unmark((x, y))
unmark((y, x))
elif father[x] != y and father[y] != x:
a = find(x)
ancestors = set()
while a not in ancestors:
ancestors.add(a)
a = find(father[a])
a = find(y)
while a not in ancestors:
a = find(father[a])
for u in [x, y]:
v = find(u)
while v != a:
fv = father[v]
exchange_weight = weight[x, y] - weight[v, fv]
if exchange_weight < min_weight:
min_weight = exchange_weight
e = (v, fv)
f = (x, y)
w = find(fv)
union(v, w, w)
v = w
return (min_weight, e, f)
inf = float('inf')
if k is None:
k = inf
weight = {(u, v): graph[u][v] for u in graph for v in graph[u]}
marked = set()
mark = marked.add
unmark = marked.discard
edges = sorted(graph.edge_set(), key=lambda (u, v): weight[(u, v)])
vertices = graph.vertices()
# arbitrary root vertex
root = vertices[0]
tree_weight, father = prim(graph, root, edge_list=False)
father[root] = root
(exchange_weight, e, f) = find_exchange(father, [], [])
heap = [(tree_weight + exchange_weight, e, f, father, [], [])]
tree_edges = sorted([(min(x, y), max(x, y)) for (x, y) in father.items() if x != y])
yield tree_weight, tree_edges
j = 1
while j < k:
(tree_weight, e, f, father, included, excluded) = heappop(heap)
if tree_weight == inf:
return
new_graph = Graph()
new_graph.add_edges(father.items())
new_graph.delete_edge(e)
new_graph.add_edge(f)
new_father = breadth_first_search_tree(new_graph, root)
new_father[root] = root
tree_edges = sorted([(min(x, y), max(x, y)) for (x, y) in new_father.items() if x != y])
yield tree_weight, tree_edges
new_tree_weight = tree_weight - weight[f] + weight[e]
included_i = included + [e]
excluded_j = excluded + [e]
(exchange_weight, e, f) = find_exchange(father, included_i, excluded)
heappush(heap, (new_tree_weight + exchange_weight, e, f, father, included_i, excluded))
(exchange_weight, e, f) = find_exchange(new_father, included, excluded_j)
heappush(heap, (tree_weight + exchange_weight, e, f, new_father, included, excluded_j))
j += 1
def setUp(self):
self.graph = Graph()
self.graph.add_edges([
('a', 'b'), ('a', 's'), ('b', 'c'), ('c', 's'),
('d', 'e'), ('d', 's'), ('e', 'd'), ('e', 's')
])
def gabow(graph, k=None):
def find_exchange(father, included, excluded):
X = NamedUnionFind(vertices)
find = X.find
union = X.union
(min_weight, e, f) = (inf, None, None)
for (x, y) in included:
# Make it so that y = father[x]
if y != father[x]:
x, y = y, x
y = find(y)
union(x, y, y)
for edge in excluded:
mark(edge)
for (x, y) in edges:
if (x, y) in marked or (y, x) in marked:
unmark((x, y))
unmark((y, x))
elif father[x] != y and father[y] != x:
a = find(x)
ancestors = set()
while a not in ancestors:
ancestors.add(a)
a = find(father[a])
a = find(y)
while a not in ancestors:
a = find(father[a])
for u in [x, y]:
v = find(u)
while v != a:
fv = father[v]
exchange_weight = weight[x, y] - weight[v, fv]
if exchange_weight < min_weight:
min_weight = exchange_weight
e = (v, fv)
f = (x, y)
w = find(fv)
union(v, w, w)
v = w
return (min_weight, e, f)
inf = float('inf')
if k is None:
k = inf
weight = {(u, v): graph[u][v] for u in graph for v in graph[u]}
marked = set()
mark = marked.add
unmark = marked.discard
edges = sorted(graph.edge_set(), key=lambda (u, v): weight[(u, v)])
vertices = graph.vertices()
# arbitrary root vertex
root = vertices[0]
tree_weight, father = prim(graph, root, edge_list=False)
father[root] = root
(exchange_weight, e, f) = find_exchange(father, [], [])
heap = [(tree_weight + exchange_weight, e, f, father, [], [])]
tree_edges = sorted([(min(x, y), max(x, y)) for (x, y) in father.items()
if x != y])
yield tree_weight, tree_edges
j = 1
while j < k:
(tree_weight, e, f, father, included, excluded) = heappop(heap)
if tree_weight == inf:
return
new_graph = Graph()
new_graph.add_edges(father.items())
new_graph.delete_edge(e)
new_graph.add_edge(f)
new_father = breadth_first_search_tree(new_graph, root)
new_father[root] = root
tree_edges = sorted([(min(x, y), max(x, y))
for (x, y) in new_father.items() if x != y])
yield tree_weight, tree_edges
new_tree_weight = tree_weight - weight[f] + weight[e]
included_i = included + [e]
excluded_j = excluded + [e]
(exchange_weight, e, f) = find_exchange(father, included_i, excluded)
heappush(heap, (new_tree_weight + exchange_weight, e, f, father,
included_i, excluded))
(exchange_weight, e, f) = find_exchange(new_father, included,
excluded_j)
heappush(heap, (tree_weight + exchange_weight, e, f, new_father,
included, excluded_j))
j += 1
def setUp(self):
self.petersen_graph = petersen_graph()
self.petersen_colors = 3
self.coprime_graph30 = coprime_pairs_graph(30)
self.coprime_graph30_colors = 11
graph = Graph()
graph.add_edges([(0, 2), (0, 4), (0, 5), (0, 8), (1, 3), (1, 4), (2, 6), (2, 8), (3, 4), (3, 7), (4, 6), (4, 7),
(4, 9), (5, 8)])
self.small_random = graph
self.small_colors = 3
self.small_classes = [[4, 8], [0, 3, 6, 9], [1, 2, 5, 7]]
graph = Graph()
graph.add_edges([(0, 4), (0, 6), (1, 2), (1, 4), (1, 8), (1, 9), (2, 3), (2, 4), (3, 5), (3, 7), (4, 6), (4, 8),
(4, 9), (5, 7), (5, 9), (6, 8), (7, 8)])
self.small_random2 = graph
self.small_colors2 = 4
self.small_classes2 = [[4, 7], [0, 2, 8, 9], [1, 5, 6], [3]]
graph = Graph()
graph.add_edges([(0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 7), (0, 8), (0, 9), (0, 10), (0, 11), (0, 12),
(0, 13), (0, 14), (0, 15), (0, 16), (0, 17), (0, 18), (0, 19), (1, 2), (1, 3), (1, 4), (1, 5),
(1, 6), (1, 7), (1, 8), (1, 9), (1, 10), (1, 12), (1, 14), (1, 15), (1, 17), (1, 18), (1, 19),
(2, 4), (2, 5), (2, 6), (2, 7), (2, 8), (2, 9), (2, 11), (2, 12), (2, 13), (2, 14), (2, 15),
(2, 17), (2, 18), (2, 19), (3, 4), (3, 5), (3, 6), (3, 7), (3, 8), (3, 9), (3, 10), (3, 11),
(3, 12), (3, 14), (3, 15), (3, 16), (3, 17), (3, 18), (4, 5), (4, 6), (4, 7), (4, 9), (4, 10),
(4, 11), (4, 13), (4, 14), (4, 15), (4, 16), (4, 17), (4, 18), (4, 19), (5, 6), (5, 7), (5, 9),
(5, 10), (5, 13), (5, 14), (5, 15), (5, 17), (5, 19), (6, 8), (6, 9), (6, 10), (6, 11),
(6, 12), (6, 13), (6, 14), (6, 15), (6, 16), (6, 17), (6, 19), (7, 8), (7, 9), (7, 12),
(7, 15), (7, 16), (7, 17), (7, 19), (8, 9), (8, 10), (8, 11), (8, 12), (8, 13), (8, 14),
(8, 16), (8, 17), (8, 18), (8, 19), (9, 10), (9, 11), (9, 14), (9, 17), (9, 18), (9, 19),
(10, 11), (10, 12), (10, 13), (10, 15), (10, 16), (10, 17), (10, 18), (10, 19), (11, 12),
(11, 13), (11, 14), (11, 15), (11, 16), (11, 17), (11, 18), (11, 19), (12, 13), (12, 14),
(12, 15), (12, 16), (12, 17), (12, 18), (12, 19), (13, 14), (13, 15), (13, 16), (13, 17),
(13, 18), (13, 19), (14, 16), (14, 17), (14, 18), (15, 17), (15, 19), (16, 18), (16, 19),
(17, 18), (18, 19)])
self.big_random = graph
self.big_colors = 10
self.big_classes = [[0, 6], [17, 19], [4, 8], [5, 11], [7, 10, 14], [2, 3], [15, 18], [9, 12], [1, 16], [13]]
class TestCliques(unittest.TestCase):
def setUp(self):
self.petersen_graph = petersen_graph()
self.random_graph10_5 = random_graph(10, 0.5)
self.random_graph100_3 = random_graph(100, 0.3)
self.petersen_cliques = [
[0, 1], [0, 4], [0, 5], [1, 2], [1, 6],
[2, 3], [2, 7], [3, 4], [3, 8], [4, 9],
[5, 7], [5, 8], [6, 8], [6, 9], [7, 9],
]
self.random_graph_edges = [
(0, 2), (0, 3), (0, 5), (0, 7),
(0, 9), (1, 2), (1, 3), (1, 5),
(1, 6), (1, 7), (1, 9), (2, 3),
(2, 5), (2, 6), (2, 7), (2, 8),
(3, 5), (3, 7), (4, 6), (4, 7),
(5, 6), (5, 7), (5, 9), (6, 7),
(6, 8), (6, 9), (7, 9),
]
self.random_graph = Graph()
self.random_graph.add_edges(self.random_graph_edges)
self.random_graph_clique = [0, 2, 3, 5, 7]
self.coprime_graph10 = coprime_pairs_graph(10)
self.coprime_graph10_weight = 30
self.coprime_graph10_clique = [1, 5, 7, 8, 9]
self.coprime_graph100 = coprime_pairs_graph(100)
self.coprime_graph100_weight = 1356
self.coprime_graph100_clique = [
1, 17, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61,
67, 71, 73, 79, 81, 83, 88, 89, 91, 95, 97,
]
def test_maximal_cliques(self):
print "Testing Bron-Kerbosh algorithm"
cliques = [sorted(e) for e in bron_kerbosch(self.petersen_graph)]
self.assertEqual(sorted(cliques), self.petersen_cliques)
print "Testing binary Bron-Kerbosh algorithm"
cliques = [sorted(e) for e in bron_kerbosch_binary(self.petersen_graph)]
self.assertEqual(sorted(cliques), self.petersen_cliques)
print "Testing Tomita algorithm"
cliques = [sorted(e) for e in tomita(self.petersen_graph)]
self.assertEqual(sorted(cliques), self.petersen_cliques)
print "Testing generic maximal cliques algorithm"
for algorithm in ['tomita', 'bron_kerbosch', 'bron_kerbosch_binary']:
cliques = [sorted(e) for e in maximal_cliques(self.petersen_graph, algorithm=algorithm)]
self.assertEqual(sorted(cliques), self.petersen_cliques)
print "Testing with random graphs"
cliques1 = list(bron_kerbosch(self.random_graph10_5))
cliques2 = list(bron_kerbosch_binary(self.random_graph10_5))
cliques3 = list(tomita(self.random_graph10_5))
self.assertEqual(len(cliques1), len(cliques2))
self.assertEqual(len(cliques2), len(cliques3))
cliques1 = list(bron_kerbosch(self.random_graph100_3))
cliques2 = list(bron_kerbosch_binary(self.random_graph100_3))
cliques3 = list(tomita(self.random_graph100_3))
self.assertEqual(len(cliques1), len(cliques2))
self.assertEqual(len(cliques2), len(cliques3))
def test_maximum_clique(self):
print "Testing Ostergard algorithm"
size, clique = ostergard(self.random_graph)
self.assertEqual(size, 5)
self.assertEqual(sorted(clique), self.random_graph_clique)
print "Testing Pardalos algorithm"
size, clique = pardalos(self.random_graph)
self.assertEqual(size, 5)
self.assertEqual(sorted(clique), self.random_graph_clique)
print "Testing generic maximum-clique algorithm"
for algorithm in ['pardalos', 'ostergard']:
size, clique = maximum_clique(self.random_graph, algorithm=algorithm)
self.assertEqual(size, 5)
self.assertEqual(sorted(clique), self.random_graph_clique)
def test_maximum_weight_clique(self):
print "Testing Maximum-weight clique algorithm"
size, clique = maximum_weight_clique(self.coprime_graph10)
self.assertEqual(size, self.coprime_graph10_weight)
self.assertEqual(sorted(clique), self.coprime_graph10_clique)
size, clique = maximum_weight_clique(self.coprime_graph100)
self.assertEqual(size, self.coprime_graph100_weight)
self.assertEqual(sorted(clique), self.coprime_graph100_clique)
def setUp(self):
self.petersen_graph = petersen_graph()
self.random_graph10_5 = random_graph(10, 0.5)
self.random_graph100_3 = random_graph(100, 0.3)
self.petersen_cliques = [
[0, 1],
[0, 4],
[0, 5],
[1, 2],
[1, 6],
[2, 3],
[2, 7],
[3, 4],
[3, 8],
[4, 9],
[5, 7],
[5, 8],
[6, 8],
[6, 9],
[7, 9],
]
self.random_graph_edges = [
(0, 2),
(0, 3),
(0, 5),
(0, 7),
(0, 9),
(1, 2),
(1, 3),
(1, 5),
(1, 6),
(1, 7),
(1, 9),
(2, 3),
(2, 5),
(2, 6),
(2, 7),
(2, 8),
(3, 5),
(3, 7),
(4, 6),
(4, 7),
(5, 6),
(5, 7),
(5, 9),
(6, 7),
(6, 8),
(6, 9),
(7, 9),
]
self.random_graph = Graph()
self.random_graph.add_edges(self.random_graph_edges)
self.random_graph_clique = [0, 2, 3, 5, 7]
self.random_graph_clique2 = [1, 5, 6, 7, 9]
self.coprime_graph10 = coprime_pairs_graph(10)
self.coprime_graph10_weight = 30
self.coprime_graph10_clique = [1, 5, 7, 8, 9]
self.coprime_graph100 = coprime_pairs_graph(100)
self.coprime_graph100_weight = 1356
self.coprime_graph100_clique = [
1,
17,
23,
29,
31,
37,
41,
43,
47,
53,
59,
61,
67,
71,
73,
79,
81,
83,
88,
89,
91,
95,
97,
]
class TestCliques(unittest.TestCase):
def setUp(self):
self.petersen_graph = petersen_graph()
self.random_graph10_5 = random_graph(10, 0.5)
self.random_graph100_3 = random_graph(100, 0.3)
self.petersen_cliques = [
[0, 1],
[0, 4],
[0, 5],
[1, 2],
[1, 6],
[2, 3],
[2, 7],
[3, 4],
[3, 8],
[4, 9],
[5, 7],
[5, 8],
[6, 8],
[6, 9],
[7, 9],
]
self.random_graph_edges = [
(0, 2),
(0, 3),
(0, 5),
(0, 7),
(0, 9),
(1, 2),
(1, 3),
(1, 5),
(1, 6),
(1, 7),
(1, 9),
(2, 3),
(2, 5),
(2, 6),
(2, 7),
(2, 8),
(3, 5),
(3, 7),
(4, 6),
(4, 7),
(5, 6),
(5, 7),
(5, 9),
(6, 7),
(6, 8),
(6, 9),
(7, 9),
]
self.random_graph = Graph()
self.random_graph.add_edges(self.random_graph_edges)
self.random_graph_clique = [0, 2, 3, 5, 7]
self.random_graph_clique2 = [1, 5, 6, 7, 9]
self.coprime_graph10 = coprime_pairs_graph(10)
self.coprime_graph10_weight = 30
self.coprime_graph10_clique = [1, 5, 7, 8, 9]
self.coprime_graph100 = coprime_pairs_graph(100)
self.coprime_graph100_weight = 1356
self.coprime_graph100_clique = [
1,
17,
23,
29,
31,
37,
41,
43,
47,
53,
59,
61,
67,
71,
73,
79,
81,
83,
88,
89,
91,
95,
97,
]
def test_maximal_cliques(self):
print "Testing Bron-Kerbosh algorithm"
cliques = [sorted(e) for e in bron_kerbosch(self.petersen_graph)]
self.assertEqual(sorted(cliques), self.petersen_cliques)
print "Testing binary Bron-Kerbosh algorithm"
cliques = [
sorted(e) for e in bron_kerbosch_binary(self.petersen_graph)
]
self.assertEqual(sorted(cliques), self.petersen_cliques)
print "Testing Tomita algorithm"
cliques = [sorted(e) for e in tomita(self.petersen_graph)]
self.assertEqual(sorted(cliques), self.petersen_cliques)
print "Testing generic maximal cliques algorithm"
for algorithm in ['tomita', 'bron_kerbosch', 'bron_kerbosch_binary']:
cliques = [
sorted(e) for e in maximal_cliques(self.petersen_graph,
algorithm=algorithm)
]
self.assertEqual(sorted(cliques), self.petersen_cliques)
print "Testing with random graphs"
cliques1 = list(bron_kerbosch(self.random_graph10_5))
cliques2 = list(bron_kerbosch_binary(self.random_graph10_5))
cliques3 = list(tomita(self.random_graph10_5))
self.assertEqual(len(cliques1), len(cliques2))
self.assertEqual(len(cliques2), len(cliques3))
cliques1 = list(bron_kerbosch(self.random_graph100_3))
cliques2 = list(bron_kerbosch_binary(self.random_graph100_3))
cliques3 = list(tomita(self.random_graph100_3))
self.assertEqual(len(cliques1), len(cliques2))
self.assertEqual(len(cliques2), len(cliques3))
def test_maximum_clique(self):
print "Testing Ostergard algorithm"
size, clique = ostergard(self.random_graph)
self.assertEqual(size, 5)
self.assertEqual(sorted(clique), self.random_graph_clique2)
print "Testing Pardalos algorithm"
size, clique = pardalos(self.random_graph)
self.assertEqual(size, 5)
self.assertEqual(sorted(clique), self.random_graph_clique)
print "Testing generic maximum-clique algorithm"
for algorithm in ['pardalos', 'ostergard']:
size, clique = maximum_clique(self.random_graph,
algorithm=algorithm)
self.assertEqual(size, 5)
def test_maximum_weight_clique(self):
print "Testing Maximum-weight clique algorithm"
size, clique = maximum_weight_clique(self.coprime_graph10)
self.assertEqual(size, self.coprime_graph10_weight)
self.assertEqual(sorted(clique), self.coprime_graph10_clique)
size, clique = maximum_weight_clique(self.coprime_graph100)
self.assertEqual(size, self.coprime_graph100_weight)
self.assertEqual(sorted(clique), self.coprime_graph100_clique)