diff options
Diffstat (limited to 'experiments/ml3.pyx')
| -rw-r--r-- | experiments/ml3.pyx | 22 |
1 files changed, 8 insertions, 14 deletions
diff --git a/experiments/ml3.pyx b/experiments/ml3.pyx index 1f46ef5..5cbe3ba 100644 --- a/experiments/ml3.pyx +++ b/experiments/ml3.pyx @@ -10,17 +10,16 @@ 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 = delta ** dist # structural = plogis(w1,delta) * plogis(w2,delta) * plogis(w3,delta) 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 - 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""" @@ -29,7 +28,7 @@ cdef DTYPE_t weight_failure(int dist, int dt, DTYPE_t alpha, DTYPE_t delta, # structural = plogis(w1,delta) * plogis(w2,delta) * plogis(w3,delta) temporal = exp(-alpha * dt) # temporal = 1. - 1. / (1. + dt/alpha)**0.01 - result = log(1. - structural) + result = log(1. - structural + structural*temporal) # print 'stnv', structural, temporal return result @@ -53,7 +52,7 @@ def ml3(dict root_victims, dict victims, dict non_victims, DTYPE_t age, # likely parent infects it failures = [weight_failure(dist, dt, alpha, delta, w1, w2, w3) for (dist, dt, w1, w2, w3) in parents] - # probs_fail[i] = sum(failures) + probs_fail[i] = sum(failures) successes = [weight_success(dist, dt, alpha, delta, w1, w2, w3) for (dist, dt, w1, w2, w3) in parents] dists = [dist for (dist, dt, w1, w2, w3) in parents] @@ -67,20 +66,15 @@ def ml3(dict root_victims, dict victims, dict non_victims, DTYPE_t age, probs[i] = prob parent_dists[i] = dists[l] parent_dts[i] = dts[l] - probs_fail[i] = failures[l] - - # print successes - # print failures - # print probs + # probs_fail[i] = failures[l] # calculate log likelihood - 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_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(0.1, 1., .1): + for beta in np.arange(0.2, 1., 1.): thresh = log(beta/(3012.*(1.-beta))) # print 'beta:', beta, 'thresh:', thresh, 'infected:', len(probs[probs>=thresh]) roots = n_roots + len(probs[probs<thresh]) @@ -89,7 +83,7 @@ def ml3(dict root_victims, dict victims, dict non_victims, DTYPE_t age, # add probability for realized edges and subtract probability these edges fail beta_add += (probs[probs>=thresh]).sum() # add probability for the seeds and non-seeds - beta_add += roots * log(beta) + len(probs[probs>=thresh]) * log(1. - beta) + beta_add += roots * log(beta/3012.) + len(probs[probs>=thresh]) * log(1. - beta) if beta_add > max_beta_add: max_beta = beta |