diff options
Diffstat (limited to 'experiments/ml2.pyx')
| -rw-r--r-- | experiments/ml2.pyx | 35 |
1 files changed, 22 insertions, 13 deletions
diff --git a/experiments/ml2.pyx b/experiments/ml2.pyx index 99c9784..dd0e7a8 100644 --- a/experiments/ml2.pyx +++ b/experiments/ml2.pyx @@ -10,8 +10,8 @@ cdef DTYPE_t weight_success(int dist, int dt, DTYPE_t alpha, DTYPE_t delta, DTYP 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 = delta/(1. + 1./(w1*lmbda) + 1./(w2*lmbda) + 1./(w3*lmbda)) + structural = delta ** dist + # structural = delta/(1. + 1./(w1*lmbda) + 1./(w2*lmbda) + 1./(w3*lmbda)) temporal = log(exp(alpha)-1.) - alpha*dt # temporal = 1. / (1. + (dt - 1.)/alpha)**0.01 - 1. / (1. + dt/alpha)**0.01 result = log(structural) + temporal @@ -22,8 +22,8 @@ cdef DTYPE_t weight_failure(int dist, int dt, DTYPE_t alpha, DTYPE_t delta, DTYP 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 = delta/(1. + 1./(w1*lmbda) + 1./(w2*lmbda) + 1./(w3*lmbda)) + structural = delta ** dist + # structural = delta/(1. + 1./(w1*lmbda) + 1./(w2*lmbda) + 1./(w3*lmbda)) temporal = exp(-alpha * dt) # temporal = 1. - 1. / (1. + dt/alpha)**0.01 result = log(1. - structural + structural * temporal) @@ -43,6 +43,7 @@ def ml2(dict root_victims, dict victims, dict non_victims, np.ndarray[DTYPE_t] probs_nv = np.zeros(len(non_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] infectors = np.zeros(n_victims, dtype=DTYPE) # loop through victims for i, parents in enumerate(victims.itervalues()): @@ -50,12 +51,13 @@ def ml2(dict root_victims, dict victims, dict non_victims, # fail to infect it, also computes the probability that its most # likely parent infects it failures = [weight_failure(dist, dt, alpha, delta, lmbda, w1, w2, w3) - for (dist, dt, w1, w2, w3) in parents] + for (prnt, dist, dt, w1, w2, w3) in parents] probs_fail[i] = sum(failures) successes = [weight_success(dist, dt, alpha, delta, lmbda, w1, w2, w3) - for (dist, dt, w1, w2, w3) in parents] - dists = [dist for (dist, dt, w1, w2, w3) in parents] - dts = [dt for (dist, dt, w1, w2, w3) in parents] + for (prnt, dist, dt, w1, w2, w3) in parents] + dists = [dist for (prnt, dist, dt, w1, w2, w3) in parents] + dts = [dt for (prnt, dist, dt, w1, w2, w3) in parents] + prnts = [prnt for (prnt, 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") @@ -63,6 +65,7 @@ def ml2(dict root_victims, dict victims, dict non_victims, prob = s - failures[l] if prob > probs[i]: probs[i] = prob + infectors[i] = prnts[l] parent_dists[i] = dists[l] parent_dts[i] = dts[l] @@ -71,7 +74,7 @@ def ml2(dict root_victims, dict victims, dict non_victims, # for each non victim node, compute the probability that all its # parents fail to infect it failures = [weight_failure(dist, dt, alpha, delta, lmbda, w1, w2, w3) - for (dist, dt, w1, w2, w3) in parents] + for (prnt, dist, dt, w1, w2, w3) in parents] probs_nv[i] = sum(failures) # calculate log likelihood @@ -81,8 +84,14 @@ def ml2(dict root_victims, dict victims, dict non_victims, roots = n_roots # print n_nodes, n_roots, n_victims, max_i, roots - print parent_dists[1:100] - print parent_dts[1:100] - print np.mean(parent_dists) - print np.mean(parent_dts) + # print parent_dists[1:100] + # print parent_dts[1:100] + # print victims.keys() + # print infectors + # print np.mean(parent_dists) + # print np.mean(parent_dts) + + with open('../../Results/infectors.csv', 'w') as infectors_file: + for i, infector in enumerate(infectors): + infectors_file.write("%s, %s\n" % ((victims.keys())[i], infector)) return (lmbda, roots, ll) |