from Esme.dgms.compute import alldgms
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,