1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
|
from cPickle import load
from itertools import product
from math import exp, log, sin
import sys
from multiprocessing import Pool
def iter_events(events):
for n, s in events.iteritems():
for t in s:
yield (n, t)
def inprod(a, b):
return tuple(float(x * y) for x, y in zip(a, b))
def approx(x):
if x > 1e-10:
return 1 - exp(-x)
else:
return x
def ll(lamb, alpha, mu):
r1 = sum(log(lamb * (1 + 0.43 * sin(0.0172 * t1 + 4.36))
+ sum(alpha / d ** 2 * mu * exp(-mu * (t1 - t2))
for (n2, t2, d) in s))
for ((n1, t1), s) in event_edges.iteritems())
r2 = sum(sum(alpha / d ** 2 * approx(mu * (nodes[n2][0] - t1))
for n2, d in edges[n1].iteritems()
if nodes[n2][0] > t1)
for (n1, t1) in iter_events(events))
r3 = lamb * sum(node[1] for node in nodes.itervalues())
return -(r1 - r2 - r3)
def get_values():
d = {}
for line in open(sys.argv[1]):
v = map(float, line.strip().split())
d[tuple(v[:3])] = v[3]
l = d.items()
l.sort(key=lambda x: x[1])
for line in open("refine.txt"):
v = map(float, line.strip().split())
d[tuple(v[:3])] = v[3]
for a, _ in l[:20]:
t = [1. / i for i in range(2, 4)] + [float(i) for i in range(1, 4)]
for b in product(t, repeat=3):
l, al, m = inprod(a, b)
if (l, al, m) in d:
continue
yield (l, al, m)
def refine():
p = Pool(5)
def aux(x):
l, a, m = x
print l, a, m, ll(l, a, m)
sys.stdout.flush()
p.map(aux, get_values())
if __name__ == "__main__":
nodes, edges, events, event_edges = load(open("data-all.pickle", "rb"))
refine()
|
