Tried ml3 that allows for multiple parents to infect.

1 files changed, 9 insertions, 7 deletions

diff --git a/experiments/ml3.pyx b/experiments/ml3.pyx
index 49b24cf..faeda92 100644
--- a/experiments/ml3.pyx
+++ b/experiments/ml3.pyx
@@ -36,6 +36,7 @@ def ml3(dict root_victims, dict victims, dict non_victims, DTYPE_t age,
         np.ndarray[DTYPE_t] probs_fail = np.zeros(n_victims, dtype=DTYPE)
         np.ndarray[DTYPE_t] parent_dists = np.zeros(n_victims, dtype=DTYPE)
         np.ndarray[DTYPE_t] parent_dts = np.zeros(n_victims, dtype=DTYPE)
+        np.ndarray[DTYPE_t] isSeed = np.ones(n_victims, dtype=DTYPE)
 
     # loop through victims
     for i, parents in enumerate(victims.itervalues()):
@@ -51,25 +52,26 @@ def ml3(dict root_victims, dict victims, dict non_victims, DTYPE_t age,
         dts = [dt for (dist, dt, w1, w2, w3) in parents]
         # find parent that maximizes log(p) - log(\tilde{p})
         # probs[i] = max(s - failures[l] for l, s in enumerate(successes)) 
-        probs[i] = float("-inf")
+        # probs[i] = 0.
         for l, s in enumerate(successes):
             prob = s - failures[l]
-            if prob > probs[i]:
-                probs[i] = prob
+            if prob > -19.523275053840013:
+                isSeed[i] = 0
+                probs[i] += prob
                 parent_dists[i] = dists[l]
                 parent_dts[i] = dts[l]
                 # probs_fail[i] = failures[l]
 
     # calculate log likelihood
-    ll =  probs_fail.sum() # add probability that all edges to all victims fail
+    ll = probs_fail.sum() # add probability that all edges to all victims fail
 
     # print 'probs', probs
     max_beta_add = float('-inf')
     # iterate over all victim nodes to find the optimal threshold
-    for beta in np.arange(0.2, 1., .1):
+    for beta in np.arange(0.00001, 1., 1.):
         thresh = log(beta/(3012.*(1.-beta)))
-        seeds = probs<thresh
-        non_seeds = probs>=thresh
+        seeds = isSeed==1
+        non_seeds = isSeed==0
         roots = n_roots + sum(seeds)
 
         beta_add = 0.