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)
import cPickle as pickle
from similarity import dmSim
pfile = open("netdata.p", "rb")
(edges, ciss, proms) = pickle.load(pfile)
pfile.close()
sims = dict()
for x in ciss.keys():
sims[x] = dict()
for y in ciss.keys():
sims[x][y] = dmSim(ciss[x].seq, ciss[y].seq)
pfile = open("dmSims.p", "wb")
pickle.dump(sims, pfile)
pfile.close()
sims2=dict()
sims3=dict()
pfile_lst=[pp1,pp2,pp3]
data_lst=[sims1,sims2,sims3]
pfile=open('netdata.p','rb')
(edges,ciss,proms)=pickle.load(pfile)
pfile.close()
for x in ciss.keys():
sims1[x]=dict()
sims2[x]=dict()
sims3[x]=dict()
for y in ciss.keys():
sims1[x][y]=swSim(ciss[x].seq,ciss[y].seq,mismatch=misscore,\
match=mscore)
sims2[x][y]=swSim(ciss[x].seq,ciss[y].seq,mismatch=misscore,\
match=mscore,useLength=True)
sims3[x][y]=dmSim(ciss[x].seq,ciss[y].seq,useLength=True)
for i in range(3):
pfile=open(pfile_lst[i],'wb')
pickle.dump(data_lst[i],pfile)
pfile.close()
