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argmax_bayesian_solver.py
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argmax_bayesian_solver.py
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import json
import csv
import itertools
from libpgm.nodedata import NodeData
from libpgm.graphskeleton import GraphSkeleton
from libpgm.discretebayesiannetwork import DiscreteBayesianNetwork
from copy import deepcopy
from os import listdir
files = listdir("bayes_net/")
CHARS = ['d', 'o', 'i', 'r', 'a', 'h', 't', 'n', 's', 'e']
truth_r = csv.reader(open("dataset/truth.dat","rb"), delimiter='\t')
data_r = open("dataset/data.dat","rb")
data_l = []
for line in data_r.readlines():
data_l.append(map(int, line.split()))
truth_l = []
for row in truth_r:
truth_l.append(row[0])
w = csv.writer(open("bayesian_outcome.txt", "wb"))
count = 0
for i in range(104):
nd = NodeData()
skel = GraphSkeleton()
nd.load('bayes_net/'+str(i)+".txt") # any input file
skel.load('bayes_net/'+str(i)+".txt")
# topologically order graphskeleton
skel.toporder()
# 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
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