'startX': "stationary"
})
fitinfo = rw.Fitinfo({
'startGraph': "windowgraph_valid",
'followtype': "avg",
'record': False,
'recorddir': "records/",
'prune_limit': 100,
'triangle_limit': 100,
'other_limit': 100
})
toygraph = rw.Toygraphs({
'graphtype': "steyvers",
'numnodes': 50,
'numlinks': 6
})
fh = open('priming_test.csv', 'w')
seed = 15
for td in toydata:
print "numx: ", td.numx
# generate data with priming and fit best graph
g, a = rw.genG(toygraph, seed=seed)
[Xs, irts, alter_graph] = rw.genX(g, td, seed=seed)
bestgraph_priming, ll = rw.uinvite(Xs,
td,
toygraph.numnodes,
fitinfo=rw.Fitinfo({
'startGraph': "windowgraph_valid",
'windowgraph_size': 2,
'windowgraph_threshold': 2,
'followtype': "avg",
'prior_samplesize': 10000,
'recorddir': "records/",
'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])
import rw
import math
# double check number of nodes is correct
graph = rw.Toygraphs({
'graphtype': "smallworld",
'numnodes': 50,
'numlinks': 4,
'probRewire': .3
})
prior = rw.genSWPrior(
graph) # same vals used in generating test_case_prior.csv
outfile = open('swvals3.csv', 'w')
with open('test_case_prior2.csv', 'r') as fh:
next(fh) # skip header row
for line in fh:
line = line.split(',')
tg = rw.hashToGraph(line[10]) # true graph
nrw = rw.hashToGraph(line[14]) # naive random walk
uinp = rw.hashToGraph(line[22]) # u-invite no prior
uip = rw.hashToGraph(line[30]) # u-invite w/ prior
numx = str(line[7])
outfile.write(numx + ',')
outfile.write(
str(math.log(rw.evalSWPrior(rw.smallworld(tg), prior))) + ',')
outfile.write(
