# toy data g,a=rw.genG(numnodes,numlinks,probRewire) Xs=[rw.genX(g) for i in range(numx)] [Xs,g,a,numnodes]=rw.trimX(trim,Xs,g) expected_irts=rw.expectedIRT(Xs,a,numnodes, beta) subj="S103" category="animals" starttime=str(datetime.now()) Xs, items, expected_irts, numnodes=readX(subj,category) # gen candidate graphs graphs=rw.genGraphs(numgraphs,theta,Xs,numnodes) graphs.append(rw.noHidden(Xs,numnodes)) # probably best starting graph #allnodes=[(i,j) for i in range(len(a)) for j in range(len(a)) if (i!=j) and (i>j)] max_converge=25 converge=0 oldbestval=0 bestgraphs=[] log=[] log.append(starttime) while converge < max_converge: graphs, bestval=rw.graphSearch(graphs,numkeep,Xs,numnodes,maxlen,jeff,expected_irts) log.append(bestval) if bestval == oldbestval: converge += 1 else:
outfile='sdt.csv' fo=open(outfile,'a', 0) # write/append to file with no buffering for line in range(1,len(dat)-1): linearr=dat[line].split(',') numnodes=int(linearr[10]) numlinks=int(linearr[11]) probRewire=float(linearr[12]) graph_seed=int(linearr[14]) g,a=rw.genG(numnodes,numlinks,probRewire,seed=graph_seed) x_seed=int(linearr[17]) numx=int(linearr[15]) [Xs,irts]=zip(*[rw.genX(g, seed=x_seed+i,use_irts=1) for i in range(numx)]) g1=rw.noHidden(Xs, numnodes) # directly connect Xs into a graph g2=rw.hashToGraph(linearr[23], numnodes) # irts g3=rw.hashToGraph(linearr[24], numnodes) # no irts sdt1=rw.costSDT(g1, a) sdt2=rw.costSDT(g2, a) sdt3=rw.costSDT(g3, a) fo.write(str(sdt1[0]) + ',' + str(sdt1[1]) + ',' + str(sdt1[2]) + ',' + str(sdt1[3]) + ',' + str(sdt2[0]) + ',' + str(sdt2[1]) + ',' + str(sdt2[2]) + ',' + str(sdt2[3]) + ',' + str(sdt3[0]) + ',' +