from cPickle import load
from math import log, exp
#import numpy as np
#from scipy.optimize import basinhopping
from itertools import product


def iter_events(events):
    for n, s in events.iteritems():
        for t in s:
            yield (n, t)


def ll(a):
    lamb, alpha, mu = a

    r1 = sum(log(lamb + sum(alpha * w * mu * exp(-mu * (t1 - t2))
                            for (n2, t2, w) in s))
             for ((n1, t1), s) in event_edges.iteritems())
    r2 = sum(sum(alpha * w * (1 - exp(-mu * (nodes[n2] - t1)))
                 for n2, w in edges[n1].iteritems() if nodes[n2] > t1)
             for (n1, t1) in iter_events(events))
    r3 = lamb * sum(nodes.itervalues())
    return -(r1 - r2 - r3)


if __name__ == "__main__":
    nodes, edges, events, event_edges = load(open("data.pickle", "rb"))
    d = {}
    for line in open("values.txt"):
        v = map(float, line.strip().split())
        d[tuple(v[:3])] = v[3]

    # lamb = [20. ** i for i in range(-15, 15)]
    # alpha = [20. ** i for i in range(-15, 15)]
    # mu = [20. ** i for i in range(-15, 15)]
    # for l, a, m in product(lamb, alpha, mu):
    #     if (l, a, m) in d:
    #         continue
    #     print l, a, m, ll((l, a, m))
    print ll((2, 10000000000., 0.000000000000000000001))
    #r = basinhopping(ll, init, disp=True, stepsize=0.1, T=10000., niter=500)
    #print r.x