# 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]) + ',' +
