def mssg(self, from_v, to_w, isMax=False):
# collect all mssg arriving at v
mess = []
neighbors = self.adj[from_v]
for n in neighbors:
if n!=to_w:
pos = self.adj[n].index(from_v)
msg = self.delta[n][pos]
mess.append(msg)
# take the the initial Psi (and log if needed)
d = copy.copy(self.factors[from_v])
if isMax==True:
d.values = np.log(d.values)
# multiply/sum by incoming messages
for ms in mess:
if isMax==True:
d = FactorOperations.sum(d, ms, False)
else:
d = FactorOperations.multiply(d, ms, True)
# marginalized to setsep vars
for n in d.variables:
if n not in (self.box[from_v] & self.box[to_w]):
if isMax==True:
d = FactorOperations.max_marginalize(d, n)
else:
d = FactorOperations.marginalize(d, n)
return d
def MAP_Word(word):
chars = len(word['gT'])
vall = [None]*chars
for i in range(chars):
vall[i] = Rvar.Rvar(i, 26)
f = []
for i in range(chars):
f.append(singletonFactor(vall[i], word['img'][i]))
for i in range(chars-1):
f.append(pairwiseFactor(vall[i], vall[i+1]))
for i in range(chars-2):
f.append(tripletFactor(vall[i], vall[i+1], vall[i+2]))
# choose the top two similar images
ss = []
for i in range(chars):
for j in range(i+1, chars):
ss.append([vall[i], vall[j], similarity(word['img'][i], word['img'][j])])
ss = sorted(ss, key = lambda x: x[2])
top1 = ss.pop()
print top1
f.append(image_simil_factor(top1[0], top1[1], top1[2]))
top2 = ss.pop()
print top2
f.append(image_simil_factor(top2[0], top2[1], top2[2]))
#alist.sort(key=lambda x: x.foo)
#f1.append(image_simil_factor(vall[i], vall[j], word['img'][i], word['img'][j]))
print '---', len(f)
cc = CliqueTree.CliqueTree(f)
print cc
cc.calibrate(isMax=True)
# BEWARE I AM assuming that I get exact unambiguous marginals
# which in the generality of problems does not have to happen
# that is why the checking at bottom is important
sol = []
for vari in vall:
for beta in cc.beta:
if vari in beta.variables:
fu = copy.copy(beta)
for g in (set(beta.variables) - set([vari])):
fu = FactorOperations.max_marginalize(fu, g)
maxi = np.max(fu.values)
sol.append(list(fu.values).index(maxi))
break
return sol
