sys.path.append('./rw')
import rw
import numpy as np
import random
import math
import sys
numnodes=15
numlinks=4
probRewire=.3
numx=3
graph_seed=1
x_seed=graph_seed
randomseed=1
g,a=rw.genG(numnodes,numlinks,probRewire,seed=graph_seed)
Xs=[rw.genX(g, seed=x_seed+i) for i in range(numx)]
def PfromB(b):
P=np.empty([numnodes,numnodes],dtype=float) # set P from b (transition matix P[to,from])
for colnum, col in enumerate(b.T):
for rownum, row in enumerate(col):
P[rownum,colnum]=math.exp(row)/sum([math.exp(i) for i in col])
return P
def regml():
#random.seed(randomseed) # only for replicability
#np.random.seed(randomseed)
# free parameters
})
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=fitinfo,
seed=seed,
debug=True)
priming_cost = rw.cost(bestgraph_priming, a)
print priming_cost
td.priming = 0.0
# fit best graph assuming no priming
bestgraph_nopriming, ll = rw.uinvite(Xs,
td,
toygraph.numnodes,
import rw
import numpy as np
steps=[]
ehs=[]
meaneh=[]
meanstep=[]
fh1 = open('ehmean.csv','w')
fh2 = open('eh.csv','w')
for i in range(10000):
print i
g,a=rw.genG(20,4,.3)
X,step=rw.genX(g,use_irts=1)
eh=rw.expectedHidden([X],a,len(a))[0]
steps.append(step)
ehs.append(eh)
meaneh.append(np.mean(eh))
meanstep.append(np.mean(step))
for num, i in enumerate(meaneh):
fh1.write(str(i) + "," + str(meanstep[num-1]) + "\n")
ehs=rw.flatten_list(ehs)
steps=rw.flatten_list(steps)
for num, i in enumerate(ehs):
fh2.write(str(i) + "," + str(steps[num-1]) + "\n")
fh1.close()
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
rw.drawMat(a, cmap=plt.cm.BuGn)
newmat = rw.drawMatChange(Xs, a, td, (0, 1), keep=0) # e.g.