dic1 = {}
k = 1
for c in data_l[i]:
dic1[str(k)] = str(c)
k += 2
print dic1
k = 2 * len(data_l[i]) - 2
dic2 = {}
word = ''
while k >= 0:
maxx = 0
char = ''
temp = deepcopy(dic2)
for c in CHARS:
temp[str(k)] = [c]
curr = bn.specificquery(temp, dic1)
if curr > maxx:
maxx = curr
dic2[str(k)] = [c]
char = c
word = char + word
k -= 2
if word == truth_l[i]:
count += 1
w.writerow([word, maxx])
print count
# load bayesian network
# load bayesian network
bn = DiscreteBayesianNetwork(skel, nd)
dic1 = {}
k = 1
for c in data_l[i]:
dic1[str(k)] = str(c)
k += 2
maxx = 0
pred = ''
for word in all_perms:
dic2 = {}
k = 0
for c in word:
dic2[str(k)] = [c]
k += 2
curr = bn.specificquery(dic2,dic1)
if curr > maxx:
maxx = curr
pred = ''.join(word)
if pred == truth_l[i]:
count += 1
print count
print pred
w.writerow([pred, maxx])
