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_hat.add_nodes_from(G.nodes())
    for node in G.nodes():
        unaccounted = cascades
        for cascade in cascades:

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()