#subs=['S1','S2','S3','S4','S5','S7','S8','S9','S10','S11','S12','S13']
toydata=rw.Toydata({
'numx': 3,
'trim': 1,
'jump': 0.0,
'jumptype': "stationary",
'priming': 0.0,
'startX': "stationary"})
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': [],
def network_properties(command):
subj_props = command['data_parameters']
command = command['network_parameters']
Xs, items, irts, numnodes = rw.readX(subj_props['subject'],
subj_props['category'],
subj_props['fullpath'])
def no_persev(x):
seen = set()
seen_add = seen.add
return [i for i in x if not (i in seen or seen_add(i))]
toydata = rw.Data({
'numx': len(Xs),
'trim': 1,
'jump': 0.0,
'jumptype': "stationary",
'priming': 0.0,
'startX': "stationary"
})
fitinfo = rw.Fitinfo({
'startGraph': "goni_valid",
'goni_size': 2,
'goni_threshold': 2,
'followtype': "avg",
'prior_samplesize': 10000,
'recorddir': "records/",
'prune_limit': 100,
'triangle_limit': 100,
'other_limit': 100
})
if command['network_method'] == "RW":
bestgraph = rw.noHidden(Xs, numnodes)
elif command['network_method'] == "Goni":
bestgraph = rw.goni(Xs, numnodes, td=toydata, valid=0, fitinfo=fitinfo)
elif command['network_method'] == "U-INVITE":
no_persev_Xs = [no_persev(x) for x in Xs
] # U-INVITE doesn't work with perseverations
bestgraph, ll = rw.uinvite(no_persev_Xs,
toydata,
numnodes,
fitinfo=fitinfo,
debug=False)
nxg = nx.to_networkx_graph(bestgraph)
node_degree = np.mean(nxg.degree().values())
nxg_json = jsonGraph(nxg, items)
clustering_coefficient = nx.average_clustering(nxg)
try:
aspl = nx.average_shortest_path_length(nxg)
except:
aspl = "disjointed graph"
return {
"type": "network_properties",
"node_degree": node_degree,
"clustering_coefficient": clustering_coefficient,
"aspl": aspl,
"graph": nxg_json
}
usf_numnodes = len(usf_items)
numsubs = 50
numlists = 3
listlength = 35
numsims = 1
#methods=['rw','goni','chan','kenett','fe']
methods = ['uinvite']
toydata = rw.Data({'numx': numlists, 'trim': listlength})
fitinfo = rw.Fitinfo({
'startGraph': "goni_valid",
'goni_size': 2,
'goni_threshold': 2,
'followtype': "avg",
'prune_limit': np.inf,
'triangle_limit': np.inf,
'other_limit': np.inf
})
#toygraphs=rw.Graphs({
# 'numgraphs': 1,
# 'graphtype': "steyvers",
# 'numnodes': 280,
# 'numlinks': 6,
# 'prob_rewire': .3})
# generate data for `numsub` participants, each having `numlists` lists of `listlengths` items
seednum = 0 # seednum=150 (numsubs*numlists) means start at second sim, etc.
import rw
toydata = rw.Toydata({
'numx': range(250, 251),
'priming': 0.3,
'jump': 0.0,
'trim': 0.7,
'jumptype': "stationary",
'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
'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]
g, a = rw.genG(toygraphs, seed=graph_seed)
[Xs,
irts.data] = zip(*[rw.genX(g, td, seed=x_seed + i) for i in range(td.numx)])
Xs = list(Xs)
irts.data = list(irts.data)
[Xs, irts.data, alter_graph] = rw.trimX(td.trim, Xs,
irts.data) # trim data when necessary
toydata = rw.Toydata({
'numx': 3,
'trim': 1,
'jump': 0.0,
'jumptype': "stationary",
'priming': 0.0,
'startX': "stationary"
})
fitinfo = rw.Fitinfo({
'startGraph': "goni_valid",
'goni_size': 2,
'goni_threshold': 2,
'followtype': "avg",
'prior_samplesize': 10000,
'prior_a': 1,
'prior_b': 1,
'recorddir': "records/",
'directed': False,
'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
})
