numx=3 trim=1 theta=.5 # probability of hiding node when generating z from x (rho function) numgraphs=100 maxlen=20 # no closed form, number of times to sum over jeff = .5 numperseed=50 edgestotweak=[1,1,1,2,3,4,5,6,7,8,9,10] numkeep=3 beta=1 # for gamma distribution when generating IRTs from hidden nodes # record start time # 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)]
bestval_orig=[]
bestgraph_irts=[]
bestgraph_noirts=[]
# WRITE DATA
outfile='sim_resultsx.csv'
f=open(outfile,'a', 0) # write/append to file with no buffering
for seed_param in range(100):
for irt_param in range(2):
graph_seed=seed_param
x_seed=seed_param
# toy data
g,a=rw.genG(numnodes,numlinks,probRewire,seed=graph_seed)
[Xs,irts]=zip(*[rw.genX(g, seed=x_seed+i,use_irts=1) for i in range(numx)])
Xs=list(Xs)
irts=list(irts)
[Xs,alter_graph]=rw.trimX(trim,Xs,g)
if irt_param:
irts=rw.stepsToIRT(irts, beta, offset)
starttime=datetime.now()
# gen candidate graphs
graphs=rw.genGraphs(numgraphs,theta,Xs,numnodes)
graphs.append(rw.noHidden(Xs,numnodes)) # probably best starting graph
converge=0