diff options
Diffstat (limited to 'experiments/ml3.pyx')
| -rw-r--r-- | experiments/ml3.pyx | 22 |
1 files changed, 3 insertions, 19 deletions
diff --git a/experiments/ml3.pyx b/experiments/ml3.pyx index d7f7ff1..1f46ef5 100644 --- a/experiments/ml3.pyx +++ b/experiments/ml3.pyx @@ -6,9 +6,6 @@ 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""" @@ -32,22 +29,20 @@ 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 + structural * temporal) + result = log(1. - structural) # print 'stnv', structural, temporal return result def ml3(dict root_victims, dict victims, dict non_victims, DTYPE_t age, DTYPE_t alpha, DTYPE_t delta): cdef: - int n_roots, n_victims, n_nodes, roots, i, dist, dt, t, l + int n_roots, n_victims, roots, i, dist, dt, t, l DTYPE_t beta, ll, beta_add, max_beta, max_beta_add list parents, failures, successes n_roots, n_victims = len(root_victims), len(victims) - n_nodes = 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) - 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) @@ -74,29 +69,18 @@ def ml3(dict root_victims, dict victims, dict non_victims, DTYPE_t age, parent_dts[i] = dts[l] probs_fail[i] = failures[l] - # loop through non-victims - # for i, parents in enumerate(non_victims.itervalues()): - # # for each non victim node, compute the probability that all its - # # parents fail to infect it - # failures = [weight_failure(dist, dt, alpha, delta, w1, w2, w3) - # 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 # print 'probs', probs max_beta_add = float('-inf') # iterate over all victim nodes to find the optimal threshold - for beta in np.arange(0.09, 1., .05): + for beta in np.arange(0.1, 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]) |