def _do_mc(self):
G = self.rgg.generate_graph()
for eps in self.epsRange:
Gerr = rg.get_errorful_subgraph(G, int(self.errFunc(eps)), eps)
self.embed.embed(Gerr)
for d in self.dRange:
x = self.embed.get_scaled(d)
vnRes = vn.vn_metrics(x, self.observed, self.notObserved)
vnRes.run()
self.vnResults[(eps,d)].append(vnRes)
mclustRes = vn.mclust_performance(x,self.block)
mclustRes.run()
self.mclustResults[(eps,d)].append(mclustRes)
kmeansRes = vn.kmeans_performance(x, self.block, self.kRange)
kmeansRes.run()
self.kmeansResults[(eps,d)].append(kmeansRes)
def _do_mc(self):
for eps in self.epsRange:
block_prob = (1-self.post1(eps)-(1-self.post0(eps)))*self.rgg.block_prob+(1-self.post0(eps))
rggErr = rg.SBMGenerator(block_prob,self.rgg.nvec)
Gerr = rggErr.generate_graph()
self.embed.embed(Gerr)
for d in self.dRange:
x = self.embed.get_scaled(d)
vnRes = vn.vn_metrics(x, self.observed, self.notObserved)
vnRes.run()
self.vnResults[(eps,d)].append(vnRes)
# mclustRes = vn.mclust_performance(x,self.block)
# mclustRes.run()
# self.mclustResults[(eps,d)].append(mclustRes)
kmeansRes = vn.kmeans_performance(x, self.block, self.kRange)
kmeansRes.run()
self.kmeansResults[(eps,d)].append(kmeansRes)
