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import numpy as np
import networkx as nx
import cascade_creation
def greedy_prediction(G, cascades):
"""
returns estimated graph
"""
G_hat = cascade_creation.InfluenceGraph(max_proba=None)
G.add_nodes_from(G.nodes())
for node in G.nodes():
unaccounted = cascades
for cascade in cascades:
time_step = 0
while not cascade[time_step][node]:
time_step += 1
def test():
"""
unit test
"""
G = cascade_creation.InfluenceGraph(max_proba = .3)
G.erdos_init(n = 100, p = 1)
import time
t0 = time.time()
print len(cascade_creation.icc_cascade(G, p_init=.1))
t1 = time.time()
print t1 - t0
if __name__=="__main__":
test()
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