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,