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from cPickle import load
from math import log, exp, sqrt, sin
from random import random, gauss
import sys
from itertools import product
from multiprocessing import Pool
GR = (sqrt(5) - 1) / 2
def gss(f, a, b, tol=1e-20):
"""golden section search to minimize along one coordinate"""
c = b - GR * (b - a)
d = a + GR * (b - a)
while abs(c - d) > tol:
fc = f(c)
fd = f(d)
if fc < fd:
b = d
d = c
c = b - GR * (b - a)
else:
a = c
c = d
d = a + GR * (b - a)
sys.stderr.write("gss:" + " ".join(map(str, [a, b, fc, fd])) + "\n")
sys.stderr.flush()
return (b + a) / 2, fc
def iter_events(events):
for n, s in events.iteritems():
for t in s:
yield (n, t)
def ll_old(lamb, alpha, mu):
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)
def ll(lamb, alpha, mu):
r1 = sum(log(lamb * (1 + 0.43 * sin(0.0172 * t1 + 4.36))
+ sum(alpha / d * mu * exp(-mu * (t1 - t2))
for (n2, t2, d) in s))
for ((n1, t1), s) in event_edges.iteritems())
r2 = sum(sum(alpha / d * (1 - exp(-mu * (nodes[n2] - t1)))
for n2, d in edges[n1].iteritems() if nodes[n2] > t1)
for (n1, t1) in iter_events(events))
r3 = lamb * sum(nodes.itervalues())
return -(r1 - r2 - r3)
def sa(x, y, z, sigma=0.5, niter=70, fc=None):
T = 0.1
e = 1.1
if fc:
fo = fc
else:
fo = ll(x, y, z)
for _ in xrange(niter):
sys.stderr.write("sa: " + " ".join(map(str, [T, sigma, x, y, z, fo]))
+ "\n")
sys.stderr.flush()
yn = max(y + gauss(0, sigma * y + 1e-10), 0)
zn = max(z + gauss(0, sigma * z + 1e-10), 0)
fn = ll(x, yn, zn)
if fn < fo or exp((fo - fn) / T) > random():
y = yn
z = zn
sigma *= 2
fo = fn
else:
sigma /= e
if sigma < 1e-5:
break
T *= 0.99
return y, z, fo
def optimize_with_sa(x, y, z, niter=200):
def f(x):
return ll(x, y, z)
# y, z = sa(x, y, z)
y, z, fc = sa(x, y, z)
for _ in xrange(niter):
x, fc = gss(f, 0, 1e-3, tol=1e-10)
y, z, fc = sa(x, y, z, fc=fc)
print x, y, z, fc
sys.stdout.flush()
def optimize_with_gss(x, y, z, niter=100):
def f(x):
return ll(x, y, z)
def g(y):
return ll(x, y, z)
def h(z):
return ll(x, y, z)
for _ in xrange(niter):
y, fc = gss(g, 0, 100, tol=1e-10)
z, fc = gss(h, 0, 100, tol=1e-10)
x, fc = gss(f, 0, 1e-3, tol=1e-10)
print x, y, z, fc
sys.stdout.flush()
def coarse_search():
d = {}
for line in open("values.txt"):
v = map(float, line.strip().split())
d[tuple(v[:3])] = v[3]
p = Pool(5)
lamb = [20. ** i for i in range(-10, 0)]
alpha = [20. ** i for i in range(-15, 15)]
mu = [20. ** i for i in range(-15, 15)]
def aux(x):
if x in d:
return
l, a, m = x
print l, a, m, ll(l, a, m)
sys.stdout.flush()
p.map(aux, product(lamb, alpha, mu))
if __name__ == "__main__":
nodes, edges, events, event_edges = load(open("data2.pickle", "rb"))
x = 1.2e-5
y = 0.002
z = 0.004
# sa(x, y, z)
with open(sys.argv[1]) as fh:
l = [map(float, line.strip().split()[:3]) for line in fh]
for e in l:
optimize_with_sa(*e)
# optimize_with_gss(x, y, z)
# print ll(x, y, z)
# coarse_search()
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