'directed': True,
'prune_limit': np.inf,
'triangle_limit': np.inf,
'other_limit': np.inf})
toygraphs=rw.Toygraphs({
'numgraphs': 1,
'graphtype': "steyvers",
'numnodes': 280,
'numlinks': 6,
'prob_rewire': .3})
irts=rw.Irts({
'data': [],
'irttype': "exgauss",
'lambda': 0.721386887,
'sigma': 6.58655566,
'irt_weight': 0.95,
'rcutoff': 20})
# USF prior
#usfnet, usfitems = rw.read_csv('./snet/USF_animal_subset.snet')
#priordict = rw.genGraphPrior([usfnet], [usfitems])
for subj in subs:
print subj
category="animals"
Xs, items, irts.data, numnodes=rw.readX(subj,category,'./Spring2015/results_cleaned.csv',ignorePerseverations=True)
# uinvite prior
priorgraphs=[]
'numlinks': 6,
'prob_rewire': .3
})
toydata = rw.Toydata({
'numx': range(5, 25),
'trim': .7,
'jump': 0.0,
'jumptype': "uniform",
'startX': "uniform"
})
irts = rw.Irts({
'data': [],
'irttype': "gamma",
'beta': (1 / 1.1),
'irt_weight': 0.9,
'rcutoff': 20
})
fitinfo = rw.Fitinfo({
'tolerance': 1500,
'startGraph': "naiverw",
'prob_multi': 1.0,
'prob_overlap': 0.5
})
x_seed = 1
graph_seed = 1
td = toydata[0]