from bayesnet import BayesNet
# other modules
import argparse
import time
from pprint import pprint
parser = argparse.ArgumentParser(
description='Probabilistic reasoner based on Bayesian networks.')
parser.add_argument('bayesnet',
type=argparse.FileType('r'),
help='an input file where the Bayesian network is defined')
parser.add_argument(
'query',
type=argparse.FileType('r'),
help='an input file where the query and evidence are defined')
parser.add_argument('--verbose',
'-v',
action='store_true',
help='explain what is being done')
args = parser.parse_args()
if __name__ == "__main__":
# measure time
start_time_program = time.time()
print('Parsing Bayesian network from ' + args.bayesnet.name + '... ',
end='')
bnp = BNParser(args.bayesnet)
from parser import BNParser
from parser import QueryParser
from bayesnet import BayesNet
bnfile = open('tests/01.bn')
qfile = open('tests/01.in')
bnparser = BNParser(bnfile)
bnparser.parse()
qparser = QueryParser(bnparser, qfile)
print('dicionario', bnparser.parsed)
qparser.parse()
print(qparser.get_var())
print(qparser.get_evidence())
import argparse
import time
parser = argparse.ArgumentParser(description='Probabilistic reasoner based on Bayesian networks.')
parser.add_argument('bayesnet', type=argparse.FileType('r'), help='an input file where the Bayesian network is defined')
parser.add_argument('query', type=argparse.FileType('r'), help='an input file where the query and evidence are defined')
parser.add_argument('--verbose', '-v', action='store_true', help='explain what is being done')
args = parser.parse_args()
if __name__ == "__main__":
# measure time
start_time_program = time.time()
print('Parsing Bayesian network from ' + args.bayesnet.name + '... ', end = '')
bnp = BNParser(args.bayesnet)
bnp.parse()
print('Done.')
print('Parsing query from ' + args.query.name + '... ', end = '')
qparser = QueryParser(bnp, args.query)
qparser.parse()
evidence = qparser.get_evidence()
vartoinf = qparser.get_var()
print('Done.\n')
# create the Bayesian network
bn = BayesNet(bnp.parsed)
# compute the poseterior probability distribution
ppd = bn.ppd([vartoinf], evidence)
