minor changes for testing

1 files changed, 18 insertions, 14 deletions

diff --git a/experiments/ml.pyx b/experiments/ml.pyx
index 9c570e9..9a786db 100644
--- a/experiments/ml.pyx
+++ b/experiments/ml.pyx
@@ -7,50 +7,54 @@ DTYPE = np.float64
 ctypedef np.float_t DTYPE_t
 
 cdef DTYPE_t plogis(DTYPE_t weight, DTYPE_t delta):
-    return 1/(1 + exp(-weight/delta))
+    return 1./(1. + exp(-weight/delta))
 
-cdef DTYPE_t weight_success(int dist, int dt, DTYPE_t alpha,
-                           DTYPE_t delta, DTYPE_t gamma):
+cdef DTYPE_t weight_success(int dist, int dt, DTYPE_t alpha, DTYPE_t delta,
+                            DTYPE_t w1, DTYPE_t w2, DTYPE_t w3):
     """weight for successful infection, exponential time model"""
     cdef DTYPE_t structural, temporal, result
     structural = delta ** (dist)
+    # structural = plogis(w1,delta) * plogis(w2,delta) * plogis(w3,delta)
     temporal = exp(-alpha * dt) * (1 - exp(-alpha))
     result = log(structural * temporal)
     return result
 
 
-cdef DTYPE_t weight_success_power(int dist, int dt, DTYPE_t alpha,
-                           DTYPE_t delta, DTYPE_t gamma):
+cdef DTYPE_t weight_success_power(int dist, int dt, DTYPE_t alpha, DTYPE_t delta,
+                                DTYPE_t w1, DTYPE_t w2, DTYPE_t w3):
     """weight for successful infection, power-law time model"""
     cdef DTYPE_t structural, temporal, result
     structural = delta ** (dist)
+    # structural = plogis(w1,delta) * plogis(w2,delta) * plogis(w3,delta)
     temporal = 1. / (1. + (dt - 1.)/alpha)**0.01 - 1. / (1. + dt/alpha)**0.01
     result = log(structural * temporal)
     return result
 
 
-cdef DTYPE_t weight_failure(int dist, int dt, DTYPE_t alpha,
-                               DTYPE_t delta, DTYPE_t gamma):
+cdef DTYPE_t weight_failure(int dist, int dt, DTYPE_t alpha, DTYPE_t delta,
+                            DTYPE_t w1, DTYPE_t w2, DTYPE_t w3):
     """weight for failed infection, exponential time model"""
     cdef DTYPE_t structural, temporal, result
     structural = delta ** (dist)
+    # structural = plogis(w1,delta) * plogis(w2,delta) * plogis(w3,delta)
     temporal = 1. - exp(-alpha * dt)
     #result = log(1. - structural)
     result = log(1. - structural * temporal)
     return result
 
 
-cdef DTYPE_t weight_failure_power(int dist, int dt, DTYPE_t alpha,
-                               DTYPE_t delta, DTYPE_t gamma):
+cdef DTYPE_t weight_failure_power(int dist, int dt, DTYPE_t alpha, DTYPE_t delta,
+                                DTYPE_t w1, DTYPE_t w2, DTYPE_t w3):
     """weight for failed infection, power-law time model"""
     cdef DTYPE_t structural, temporal, result
     structural = delta ** (dist)
+    # structural = plogis(w1,delta) * plogis(w2,delta) * plogis(w3,delta)
     temporal = 1.  - 1. / (1. + dt/alpha)**0.01
     result = log(1. - structural * temporal)
     return result
 
 def ml(dict root_victims, dict victims, dict non_victims, DTYPE_t age,
-       DTYPE_t alpha, DTYPE_t delta, DTYPE_t gamma=10):
+       DTYPE_t alpha, DTYPE_t delta):
     cdef:
         int n_roots, n_victims, n_nodes, roots, i, dist, dt, t, l
         DTYPE_t beta, ll, beta2
@@ -67,19 +71,19 @@ def ml(dict root_victims, dict victims, dict non_victims, DTYPE_t age,
         # for each victim node i, compute the probability that all its parents
         # fail to infect it, also computes the probability that its most
         # likely parent infects it
-        failures = [weight_failure(dist, dt, alpha, delta, gamma)
+        failures = [weight_failure(dist, dt, alpha, delta, w1, w2, w3)
                     for (dist, dt, w1, w2, w3) in parents]
         probs_fail[i] = sum(failures)
-        successes = [weight_success(dist, dt, alpha, delta, gamma)
+        successes = [weight_success(dist, dt, alpha, delta, w1, w2, w3)
                      for (dist, dt, w1, w2, w3) in parents]
-        # find parent that maximizes p/\tilde{p}
+        # find parent that maximizes log(p) - log(\tilde{p})
         probs[i] = max(s - failures[l] for l, s in enumerate(successes)) 
 
     # loop through non-victims
     for i, parents in enumerate(non_victims.itervalues()):
         # for each non victim node, compute the probability that all its
         # parents fail to infect it
-        failures = [weight_failure(dist, dt, alpha, delta, gamma)
+        failures = [weight_failure(dist, dt, alpha, delta, w1, w2, w3)
                     for (dist, dt, w1, w2, w3) in parents]
         probs_nv[i] = sum(failures)