Incorporate the time varying model for the background rate

1 files changed, 9 insertions, 8 deletions

diff --git a/hawkes/main.py b/hawkes/main.py
index d98f6a0..a922812 100644
--- a/hawkes/main.py
+++ b/hawkes/main.py
@@ -1,11 +1,9 @@
 from cPickle import load
-from math import log, exp, sqrt
+from math import log, exp, sqrt, sin
 from random import random, gauss
 import sys
 from itertools import product
 from multiprocessing import Pool
-# import numpy as np
-# from scipy.optimize import basinhopping
 
 GR = (sqrt(5) - 1) / 2
 
@@ -46,8 +44,9 @@ def ll_old(lamb, alpha, mu):
 
 
 def ll(lamb, alpha, mu):
-    r1 = sum(log(lamb + sum(alpha / d * mu * exp(-mu * (t1 - t2))
-                            for (n2, t2, d) in s))
+    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)
@@ -78,7 +77,7 @@ def sa(x, y, z, sigma=0.1, niter=100):
 
 
 def optimize_with_sa(x, y, z):
-    #y, z = sa(x, y, z)
+    # y, z = sa(x, y, z)
     while True:
         x = gss(f, 0, 1, tol=1e-10)
         y, z = sa(x, y, z)
@@ -110,6 +109,7 @@ def coarse_search():
             return
         l, a, m = x
         print l, a, m, ll(l, a, m)
+        sys.stdout.flush()
 
     p.map(aux, product(lamb, alpha, mu))
     # for l, a, m in product(lamb, alpha, mu):
@@ -136,7 +136,8 @@ if __name__ == "__main__":
     y = 1.
     z = 1.
     #x, y, z = 1.8387870804e-05, 0.0383954874502, 0.00108402386463
-    x, y, z = 0.000125, 0.000125, 0.000125
+    x, y, z = 1.83e-5, 0.00125, 0.00125
     #optimize_with_sa(x, y, z)
     #optimize_with_gss(x, y, z)
-    coarse_search()
+    print ll(x, y, z)
+    #coarse_search()