diff options
Diffstat (limited to 'experiments/ml.pyx')
| -rw-r--r-- | experiments/ml.pyx | 18 |
1 files changed, 10 insertions, 8 deletions
diff --git a/experiments/ml.pyx b/experiments/ml.pyx index cc79609..853c934 100644 --- a/experiments/ml.pyx +++ b/experiments/ml.pyx @@ -19,7 +19,7 @@ cdef DTYPE_t weight_success(int dist, int dt, DTYPE_t alpha, DTYPE_t delta, if exp(-alpha*dt)==0.: print 'UNDERFLOW ERROR' # temporal = 1. / (1. + (dt - 1.)/alpha)**0.01 - 1. / (1. + dt/alpha)**0.01 result = log(structural * temporal) - # print 'st', structural, temporal + print 'st', structural, temporal return result cdef DTYPE_t weight_failure(int dist, int dt, DTYPE_t alpha, DTYPE_t delta, @@ -31,7 +31,7 @@ cdef DTYPE_t weight_failure(int dist, int dt, DTYPE_t alpha, DTYPE_t delta, temporal = exp(-alpha * dt) # temporal = 1. - 1. / (1. + dt/alpha)**0.01 result = log(1. - structural + structural * temporal) - # print 'stnv', structural, temporal + print 'stnv', structural, temporal return result def ml(dict root_victims, dict victims, dict non_victims, DTYPE_t age, @@ -41,7 +41,7 @@ def ml(dict root_victims, dict victims, dict non_victims, DTYPE_t age, DTYPE_t beta, ll, beta2 list parents, failures, successes n_roots, n_victims = len(root_victims), len(victims) - n_nodes = 148152 + n_nodes = 4#148152 cdef: np.ndarray[DTYPE_t] probs = np.zeros(n_victims, dtype=DTYPE) np.ndarray[DTYPE_t] probs_fail = np.zeros(n_victims, dtype=DTYPE) @@ -80,21 +80,23 @@ def ml(dict root_victims, dict victims, dict non_victims, DTYPE_t age, max_beta_add = float('-inf') # iterate over all victim nodes to find the optimal threshold for i in xrange(0, n_victims+1, 1): + print roots = n_roots + n_victims - i - beta = float(roots)/float(n_nodes) + beta = 1. / (1. + exp(-probs[i])) + # beta = float(roots)/float(n_nodes) thresh = log(beta/(1.-beta)) - # print 'thresh:', thresh + print 'thresh:', thresh # add probability for realized edges and subtract probability these edges fail - beta_add = (probs[probs>thresh]).sum() - # print 'len(probs[probs>thresh]):', len(probs[probs>thresh]) + beta_add = (probs[probs>=thresh]).sum() + print 'len(probs[probs>=thresh]):', len(probs[probs>=thresh]) # add probability for the seeds and non-seeds beta_add += roots * log(beta) + (n_nodes-roots) * log(1 - beta) if beta_add > max_beta_add: max_i = i max_beta_add = beta_add - print max_i, max_beta_add + print 'i:', max_i, 'add:', max_beta_add, 'roots:', roots else: print i |