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)