diff options
Diffstat (limited to 'experiments/ml3.pyx')
| -rw-r--r-- | experiments/ml3.pyx | 16 |
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. |