from Esme.dgms.format import dgms2swdgms
from Esme.dgms.kernel import sw_parallel
from Esme.embedding.lap import LaplacianEigenmaps
from Esme.graph.egograph import egograph
from Esme.graph.function import fil_strategy
from Esme.graph.generativemodel import sbm2
from Esme.ml.svm import classifier
if __name__ == '__main__':
radius, zigzag, fil, n1, n2 = 1, True, 'deg', 150, 150
fil_method = 'combined'
g, labels = sbm2(n1=n1, n2=n2, p=0.5, q=0.2)
lp = LaplacianEigenmaps(d=1)
lp.learn_embedding(g, weight='weight')
lapfeat = lp.get_embedding()
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='',
fil_method = args.fil_method
zigzag = True if fil_method == 'combined' else False
edge_kwargs = {'h': 0.3, 'edgefunc': 'edge_prob'}
gs1 = sbms(n=n, n1=100, n2=50, p=p, q=q)
gs2 = sbms(n=n, n1=75, n2=75, p=p, q=q)
# gs3 = sbms(n=n, n1=75, n2=75, p=p_, q=q_)
# gs3 = sbms(n=n, n1=50, n2=50, p=p, q=q)
gs = gs2 + gs1
labels = [1] * n + [2] * n
plt.title('p: %s, q: %s' % (p, q))
for i in range(len(gs)):
g = gs[i]
lp = LaplacianEigenmaps(d=1)
lp.learn_embedding(g, weight='weight')
lapfeat = lp.get_embedding() # lapfeat is an array
# viz = True if i%100==0 else False
# plt.subplot(2, 1, 1+i//100)
# plt.plot(lapfeat)
# plt.show()
gs[i] = fil_strategy(g,
lapfeat,
method=fil_method,
viz_flag=False,
**edge_kwargs)
# plt.show()
# sys.exit()
# plot node fv value