def pdfeat(g,
fil='deg',
dataset='wikipedia',
radius=1,
norm_flag=True,
recomp=True):
n2g = network2g(dataset=dataset,
fil=fil,
norm_flag='yes',
sub=True,
recomp=recomp)
g = n2g.compute(g)
n_node = len(g)
ego = egograph(g,
radius=radius,
n=n_node,
recompute_flag=recomp,
norm_flag=norm_flag)
ego.emb_file(dataset=dataset)
egographs = ego.egographs(method='batch_parallel')
dgms = alldgms(egographs,
radius=radius,
dataset=dataset,
recompute_flag=recomp,
method='serial',
n=n_node) # can do parallel computing
pdvector = pdemb(dgms, dataset, recompute_flag=True, norm=norm_flag)
return pdvector
lapdist = cdist(lapfeat, lapfeat, metric='euclidean')
kwargs = {'h': 0.3}
g = fil_strategy(g, lapfeat, method=fil_method, viz_flag=False, **kwargs)
ego = egograph(g,
radius=radius,
n=len(g),
recompute_flag=True,
norm_flag=True,
print_flag=False)
egographs = ego.egographs(method='serial')
dgms = alldgms(egographs,
radius=radius,
dataset='',
recompute_flag=True,
method='serial',
n=n1 + n2,
zigzag=zigzag) # compute dgms in parallel
swdgms = dgms2swdgms(dgms)
kwargs = {'bw': 1, 'n_directions': 10}
sw_kernel, _ = sw_parallel(swdgms,
swdgms,
kernel_type='sw',
parallel_flag=True,
**kwargs)
clf = classifier(np.zeros((n1 + n2, 10)),
labels,
method=None,
kernel=sw_kernel)
# plt.show()
# sys.exit()
# plot node fv value
# viz fv value
# val = dict(nx.get_node_attributes(gs[i], 'fv')).values()
# plt.plot(val)
# plt.title('q: %s, i: %s'%(q, i))
# plt.show()
# sys.exit()
print('Finish computing lapfeat')
dgms = alldgms(gs,
radius=float('inf'),
dataset='',
recompute_flag=True,
method='serial',
n=2 * n,
zigzag=zigzag) # compute dgms in parallel
print('Finish computing dgms')
swdgms = dgms2swdgms(dgms)
feat_kwargs = {'n_directions': 10, 'bw': 1}
sw_kernel, _ = sw_parallel(swdgms,
swdgms,
kernel_type='sw',
parallel_flag=True,
**feat_kwargs)
clf = classifier(np.zeros((len(labels), 10)),
labels,
method=None,