+ "_anchors"
+ str(N)
+ "."
+ str(complexity)
+ ".pk"
)
)
counter = {}
for q in queries:
counter[q] = moments[tuple(sorted(q))].transpose(index_sort(q))
CPDs = {}
lCPDs = {}
for k, val in counter.items():
CPDs[header[k[0]], tuple([header[z] for z in k[1:]])] = create_CPD(np.array(val + 1e-6, dtype=float))
model = models.TreeModel(CPDs, latents, format="CPDs")
for k in sorted(model.lCPD):
print k, model.lCPD[k]
model_ll = 0
data = file(networkdir + "/samples/" + str(N) + "_samples.dat").readlines()
def eval(dat):
if len(dat) == 0:
return 0
dat = dat.split()[2:]
Y = [int(str(z) in dat) for z in labels]
res,val,steps,gap = expGrad(_f, x_indep.copy(), 1e-5, verbose=False, lower=0)
#print 'X', np.array(res).reshape(original_shape)
return tuple(new_k), np.array(res).reshape(original_shape), val
CPDs = {}
for j in labels:
parents = [z for z in labels if (labels.index(z),labels.index(j)) in edges]
t,s = tags[labels.index(j)], tuple([tags[labels.index(i)] for i in parents])
key = tuple([j] + parents)
first_index = sorted(key).index(j)
other_indices = [sorted(key).index(p) for p in parents]
transpose_order = tuple([first_index]+other_indices)
m = marginals[tuple(sorted(key))].transpose(transpose_order)
smoothing = 0#10**(-6)
CPDs[t,s] = create_CPD(m, smoothing=smoothing, verbose=False)
tree = TreeModel(CPDs, #dictionary of CPDs
tags, #variable ids
format="CPDs" #latent structure already holds cpds
)
anchor_failures = {}
anchor_noise = {}
anchor_dict = {}
for l,a in anchors.items():
anchor_dict[header[l]] = header[a]
print 'a is', a
#noise[a] holds P(anchor | latent)
for j in xrange(L):
parents_of[latents[j]] = [latents[i] for i in np.nonzero(adj[:,j])[0]]
children_of[latents[j]] = [latents[i] for i in np.nonzero(adj[j,:])[0]]
queries.append(tuple([inv_header[latents[j]]] + [inv_header[i] for i in parents_of[latents[j]]]))
moments = pickle.load(file(networkdir+'/pickles/estimated_moments.'+'.'.join(args)+'.'+anchor_source+'_anchors'+str(N)+'.'+str(complexity)+'.pk'))
counter = {}
for q in queries:
counter[q] = moments[tuple(sorted(q))].transpose(index_sort(q))
CPDs = {}
lCPDs = {}
for k,val in counter.items():
CPDs[header[k[0]], tuple([header[z] for z in k[1:]])] = create_CPD(np.array(val + 1e-6, dtype=float))
model = models.TreeModel(CPDs, latents, format='CPDs')
for k in sorted(model.lCPD):
print k, model.lCPD[k]
model_ll = 0
data = file(networkdir+'/samples/'+str(N)+'_samples.dat').readlines()
def eval(dat):
if len(dat) == 0:
return 0
dat = dat.split()[2:]
Y = [int(str(z) in dat) for z in labels]
e = eval_likelihood(model, Y, debug=True)
return tuple(new_k), np.array(res).reshape(original_shape), val
CPDs = {}
for j in labels:
parents = [
z for z in labels if (labels.index(z), labels.index(j)) in edges
]
t, s = tags[labels.index(j)], tuple(
[tags[labels.index(i)] for i in parents])
key = tuple([j] + parents)
first_index = sorted(key).index(j)
other_indices = [sorted(key).index(p) for p in parents]
transpose_order = tuple([first_index] + other_indices)
m = marginals[tuple(sorted(key))].transpose(transpose_order)
smoothing = 0 #10**(-6)
CPDs[t, s] = create_CPD(m, smoothing=smoothing, verbose=False)
tree = TreeModel(
CPDs, #dictionary of CPDs
tags, #variable ids
format="CPDs" #latent structure already holds cpds
)
anchor_failures = {}
anchor_noise = {}
anchor_dict = {}
for l, a in anchors.items():
anchor_dict[header[l]] = header[a]
print 'a is', a
#noise[a] holds P(anchor | latent)
