def __init__(self,graph, source, sink):
self.graph = graph
self.source = source
self.sink = sink
self.double_passage_graph = nx.DiGraph()
for n in self.graph.nodes():
for m in self.graph.nodes():
self.double_passage_graph.add_node((n, m))
for v in self.double_passage_graph.nodes_iter():
for w in self.double_passage_graph.nodes_iter():
if self.graph.has_edge(v[0], w[0]) and self.graph.has_edge(v[1], w[1]):
self.double_passage_graph.add_edge(v, w)
self.double_passage_graph[v][w]['prob'] = self.graph[v[0]][w[0]]['prob'] * self.graph[v[1]][w[1]]['prob']
self.probability = {}
self.distributions = {}
self.maximum_collisions = get_longest_path_length(self.graph, self.source, self.sink)
for n in self.double_passage_graph.nodes_iter():
self.distributions[n] = {}
self.probability[n] = 0.0
if self.double_passage_graph.in_degree(n) == 0:
for i in xrange(self.maximum_collisions + 1):
self.distributions[n][i] = 0.0
self.distributions[(self.source, self.source)][1] = 1.0
self.probability[(self.source, self.source)] = 1.0
def get_sampled_distribution_of_collisions(G, source, sink, number):
count = utilities.get_longest_path_length(G, source, sink)
collision_dictionary = [0] * (count + 1)
for collision in simulate_double_runs(G, source, sink, number):
collision_dictionary[collision] = collision_dictionary[collision] + 1
return collision_dictionary
def test_get_longest_path_length(self):
self.assertEqual(5, mc.get_longest_path_length(self.path_graph, self.path_graph_sources[0], self.path_graph_sinks[0]), "should return 5 for a path graph with")
self.assertEqual(15, mc.get_longest_path_length(s.create_random_dag(15), 0, 14))
