Hello, I have been running a piece of code from the cookbook on a dataset of mine. I last ran it a year ago when it worked fine, now I however am getting error messages. The first one is "Segmentation fault (core dumped)" which I think is happening during a call to "state = gt.minimize_nested_blockmodel_dl(g, deg_corr=False,overlap=True)". The other one is "terminate called after throwing an instance of 'std::length_error' what(): vector::_M_default_append Aborted (core dumped)" which seems to be happening during "gt.mcmc_equilibrate(state,force_niter=20000, mcmc_args=dict(niter=10),callback=collect_marginals)". I am running the latest apt-get version of graph-tool on python2 on Ubuntu 16.04. I realise this may not be worth much without the data set (which I unfortunately can't share) but I thought I would check whether anybody has observed similar issue (my theory is that given it has worked in the past it might be linked to a version update). I will see if I can get a dataset for an MWE in the meantime. The code I was running was: import graph_tool.all as gt import numpy as np import cPickle as pickle import timeit g = gt.load_graph('graph_no_multi_1930.gt') with open('model_selection_results_1930.dat','a') as output: deg_corr = False overlap = True nL = 10 # Initialize the Markov chain from the "ground state" state = gt.minimize_nested_blockmodel_dl(g, deg_corr=deg_corr,overlap=overlap) dl = state.entropy() temp = "Description length: " temp+= str(dl) temp+='\n' output.write(temp) output.flush() print 'minimised state' bs = state.get_bs() # Get hierarchical partition. bs += [np.zeros(1)] * (nL - len(bs)) # Augment it to L = 10 with # single-group levels. state = state.copy(bs=bs, sampling=True) dls = [] # description length history vm = [None] * len(state.get_levels()) # vertex marginals em = None # edge marginals def collect_marginals(s): global vm, em levels = s.get_levels() vm = [sl.collect_vertex_marginals(vm[l]) for l, sl in enumerate(levels)] em = levels[0].collect_edge_marginals(em) dls.append(s.entropy()) # Now we collect the marginal distributions for exactly 200,000 sweeps print 'equilibrating' start=timeit.default_timer() gt.mcmc_equilibrate(state, force_niter=20000, mcmc_args=dict(niter=10), callback=collect_marginals) duration=timeit.default_timer()-start print 'duration for equilibrating: ', duration S_mf = [gt.mf_entropy(sl.g, vm[l]) for l, sl in enumerate(state.get_levels())] S_bethe = gt.bethe_entropy(g, em)[0] L = -np.mean(dls) val1 = L+sum(S_mf) val2 = L + S_bethe + sum(S_mf[1:]) temp = "Model evidence for nested blockmodel, deg_corr = " temp+=str(deg_corr)+', overlap = '+str(overlap) temp+=':'+str(val1)+"(mean field)," temp+=str(val2)+"(Bethe)"+'\n' output.write(temp) output.flush() with open('raw_results.dat','w') as f: f.write('DL: ') f.write(str(L)) f.write('\n') f.write('S_mf: ') f.write(str(sum(S_mf))) f.write('\n') f.write('S_bethe: ') f.write(str(S_bethe)) f.write('\n') f.write('S_bethe + sum(S_mf[1:]): ') f.write(str(S_bethe + sum(S_mf[1:]))) Best wishes, Philipp -- Sent from: http://main-discussion-list-for-the-graph-tool-project.982480.n3.nabble.com/