updated calculation of structural component with edge weights, and to

penalize less for longer distances

1 files changed, 7 insertions, 17 deletions

diff --git a/experiments/ml2.pyx b/experiments/ml2.pyx
index 95f6f34..fb2ec0d 100644
--- a/experiments/ml2.pyx
+++ b/experiments/ml2.pyx
@@ -6,20 +6,17 @@ from libc.math cimport log, exp
 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))
-
 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 = dist * log(delta)
-    # structural = plogis(w1,delta) * plogis(w2,delta) * plogis(w3,delta)
+    # structural = delta ** dist
+    structural = delta/(1. + exp(-w1*lmbda) + exp(-w2*lmbda) + exp(-w3*lmbda))
     temporal = log(exp(alpha)-1.) - alpha*dt
     # temporal = 1 - exp(-alpha*dt)
     # if exp(-alpha*dt)==0.: print 'UNDERFLOW ERROR'
     # temporal = 1. / (1. + (dt - 1.)/alpha)**0.01 - 1. / (1. + dt/alpha)**0.01
-    result = structural + temporal
+    result = log(structural) + temporal
     # print 'st', structural, temporal
     return result
 
@@ -27,8 +24,8 @@ 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)
+    # structural = delta ** dist
+    structural = delta/(1. + exp(-w1/lmbda) + exp(-w2/lmbda) + exp(-w3/lmbda))
     temporal = exp(-alpha * dt)
     # temporal = 1.  - 1. / (1. + dt/alpha)**0.01
     result = log(1. - structural + structural * temporal)
@@ -40,7 +37,7 @@ def ml2(dict root_victims, dict victims, dict non_victims, DTYPE_t age,
     print 'ML2'
     cdef:
         int n_roots, n_victims, roots, i, dist, dt, t, l
-        DTYPE_t beta, ll
+        DTYPE_t ll
         list parents, failures, successes
     n_roots, n_victims = len(root_victims), len(victims)
     cdef:
@@ -80,20 +77,13 @@ def ml2(dict root_victims, dict victims, dict non_victims, DTYPE_t age,
                     for (dist, dt, w1, w2, w3) in parents]
         probs_nv[i] = sum(failures)
 
-    # print successes
-    # print failures
-    # print probs
-
     # calculate log likelihood
-    # probs.sort(); probs = probs[::-1] # sort probs in descending order
-    # cdef:
-    #     np.ndarray[DTYPE_t] cums = probs.cumsum()
     ll =  probs_fail.sum() # add probability that all edges to victims fail
     ll += probs_nv.sum() # add probability that all edges to non_victims fail
     ll += probs.sum() # add probability for realized edges and subtract probability these edges fail
 
     roots = n_roots
-    beta = 0
+    beta = float('nan')
     # print n_nodes, n_roots, n_victims, max_i, roots
     print parent_dists[1:100]
     print parent_dts[1:100]