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Diffstat (limited to 'ic_experiments/ml2.pyx')
| -rw-r--r-- | ic_experiments/ml2.pyx | 95 |
1 files changed, 95 insertions, 0 deletions
diff --git a/ic_experiments/ml2.pyx b/ic_experiments/ml2.pyx new file mode 100644 index 0000000..1458683 --- /dev/null +++ b/ic_experiments/ml2.pyx @@ -0,0 +1,95 @@ +# 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) |