def doEntropyWeights(ee,e,ciss,link_func=maxSim,proms=None):
precs=[]
recalls=[]
weights=getEntropyWeights(e,ciss)
cis_keys=ciss.keys()
sims=dict()
for x in cis_keys:
sims[x]=dict()
for y in cis_keys:
sims[x][y]=dmSim(ciss[x].seq,ciss[y].seq\
,w1=weights[x],w2=weights[y]\
,useLength=True)
ss=flatenDict(netAlignInfer(link_func,e,sims))
for thresh in np.arange(0,1,0.1):
(p,r)=prEval(ee,ss,thresh)
precs.append(p)
recalls.append(r)
return (precs,recalls)
dmSimsL=getPickle(pp3)
'''
# Repeats of experiments
N=5
ratio=0.3
x=[]
y=[]
x2=[]
y2=[]
x3=[]
y3=[]
ee=flatenDictList(edges)
for exp_i in range(N):
e=mdf.global_remove(edges,0.3)
scores1=netAlignInfer(maxSim,e,nwSims)
scores2=netAlignInfer(maxSim,e,nwSimsL)
scores3=netAlignInfer(maxSim,e,dmSimsL)
ss1=flatenDict(scores1)
ss2=flatenDict(scores2)
ss3=flatenDict(scores3)
for thresh in np.arange(0,1,0.1):
result=prEval(ee,ss1,thresh)
result2=prEval(ee,ss2,thresh)
result3=prEval(ee,ss3,thresh)
x.append(result[0])
y.append(result[1])
x2.append(result2[0])
y2.append(result2[1])
x3.append(result3[0])
