diff options
Diffstat (limited to 'jpa_test/algorithms.py')
| -rw-r--r-- | jpa_test/algorithms.py | 10 |
1 files changed, 6 insertions, 4 deletions
diff --git a/jpa_test/algorithms.py b/jpa_test/algorithms.py index 99430b6..7cdf093 100644 --- a/jpa_test/algorithms.py +++ b/jpa_test/algorithms.py @@ -3,28 +3,30 @@ import networkx as nx import cascade_creation from collections import Counter +from itertools import izip + def greedy_prediction(G, cascades): """ - Returns estimated graph + Returns estimated graph from Greedy algorithm in "Learning Epidemic ..." """ G_hat = cascade_creation.InfluenceGraph(max_proba=None) G_hat.add_nodes_from(G.nodes()) for node in G_hat.nodes(): unaccounted = np.ones(len(cascades), dtype=bool) - for t, cascade in zip(xrange(len(cascades)), cascades): + for t, cascade in izip(xrange(len(cascades)), cascades): if not cascade.infection_time(node) or \ cascade.infection_time(node)[0] == 0: unaccounted[t] = False while unaccounted.any(): - tmp = [cascade for boolean, cascade in zip(unaccounted, + tmp = [cascade for boolean, cascade in izip(unaccounted, cascades) if boolean] parents = Counter() for cascade in tmp: parents += cascade.candidate_infectors(node) parent = parents.most_common(1)[0][0] G_hat.add_edge(parent, node) - for t, cascade in zip(xrange(len(cascades)), cascades): + for t, cascade in izip(xrange(len(cascades)), cascades): if (cascade.infection_time(parent) == \ [item - 1 for item in cascade.infection_time(node)]): unaccounted[t] = False |