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Diffstat (limited to 'experiments/ml2.pyx')
| -rw-r--r-- | experiments/ml2.pyx | 95 |
1 files changed, 0 insertions, 95 deletions
diff --git a/experiments/ml2.pyx b/experiments/ml2.pyx deleted file mode 100644 index 1458683..0000000 --- a/experiments/ml2.pyx +++ /dev/null @@ -1,95 +0,0 @@ -# cython: boundscheck=False, cdivision=True -import numpy as np -cimport numpy as np -from libc.math cimport log, exp - -DTYPE = np.float64 -ctypedef np.float_t DTYPE_t - -cdef DTYPE_t weight_success(int dist, int dt, DTYPE_t alpha, DTYPE_t delta, DTYPE_t lmbda, - 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)) - 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 - return result - -cdef DTYPE_t weight_failure(int dist, int dt, DTYPE_t alpha, DTYPE_t delta, DTYPE_t lmbda, - 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)) - temporal = exp(-alpha * dt) - # temporal = 1. - 1. / (1. + dt/alpha)**0.01 - result = log(1. - structural + structural * temporal) - return result - -def ml2(dict root_victims, dict victims, dict non_victims, - DTYPE_t alpha, DTYPE_t delta, DTYPE_t lmbda): - cdef: - int n_roots, n_victims, roots, i, dist, dt, t, l - DTYPE_t ll - list parents, failures, successes - n_roots, n_victims = len(root_victims), len(victims) - 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) - np.ndarray[DTYPE_t] infectors = np.zeros(n_victims, dtype=DTYPE) - - # loop through victims - for i, parents in enumerate(victims.itervalues()): - # for each victim node i, compute the probability that all its parents - # 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 (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 (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") - for l, s in enumerate(successes): - 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] - - # 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, lmbda, w1, w2, w3) - for (prnt, dist, dt, w1, w2, w3) in parents] - probs_nv[i] = sum(failures) - - # 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.sum() # add probability for realized edges and subtract probability these edges fail - - roots = n_roots - # print n_nodes, n_roots, n_victims, max_i, roots - # 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) |