diff options
Diffstat (limited to 'experiments/ml2.pyx')
| -rw-r--r-- | experiments/ml2.pyx | 26 |
1 files changed, 12 insertions, 14 deletions
diff --git a/experiments/ml2.pyx b/experiments/ml2.pyx index fb2ec0d..99c9784 100644 --- a/experiments/ml2.pyx +++ b/experiments/ml2.pyx @@ -6,35 +6,32 @@ 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, +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. + exp(-w1*lmbda) + exp(-w2*lmbda) + exp(-w3*lmbda)) + structural = delta/(1. + 1./(w1*lmbda) + 1./(w2*lmbda) + 1./(w3*lmbda)) temporal = log(exp(alpha)-1.) - alpha*dt - # temporal = 1 - exp(-alpha*dt) - # 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 return result -cdef DTYPE_t weight_failure(int dist, int dt, DTYPE_t alpha, DTYPE_t delta, +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. + exp(-w1/lmbda) + exp(-w2/lmbda) + exp(-w3/lmbda)) + 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) # print 'stnv', structural, temporal return result -def ml2(dict root_victims, dict victims, dict non_victims, DTYPE_t age, - DTYPE_t alpha, DTYPE_t delta): - print 'ML2' +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 @@ -52,10 +49,10 @@ def ml2(dict root_victims, dict victims, dict non_victims, DTYPE_t age, # 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, w1, w2, w3) + failures = [weight_failure(dist, dt, alpha, delta, lmbda, w1, w2, w3) for (dist, dt, w1, w2, w3) in parents] probs_fail[i] = sum(failures) - successes = [weight_success(dist, dt, alpha, delta, w1, w2, w3) + successes = [weight_success(dist, dt, alpha, delta, lmbda, w1, w2, w3) for (dist, dt, w1, w2, w3) in parents] dists = [dist for (dist, dt, w1, w2, w3) in parents] dts = [dt for (dist, dt, w1, w2, w3) in parents] @@ -73,7 +70,7 @@ def ml2(dict root_victims, dict victims, dict non_victims, DTYPE_t age, 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) + failures = [weight_failure(dist, dt, alpha, delta, lmbda, w1, w2, w3) for (dist, dt, w1, w2, w3) in parents] probs_nv[i] = sum(failures) @@ -83,8 +80,9 @@ def ml2(dict root_victims, dict victims, dict non_victims, DTYPE_t age, ll += probs.sum() # add probability for realized edges and subtract probability these edges fail roots = n_roots - beta = float('nan') # print n_nodes, n_roots, n_victims, max_i, roots print parent_dists[1:100] print parent_dts[1:100] - return (beta, roots, ll) + print np.mean(parent_dists) + print np.mean(parent_dts) + return (lmbda, roots, ll) |