message[BN.vars[var].index(vals_assignment[tgt_index])] += factor.funct(vals_assignment) * prod_value
incoming_messages[var][edges[var].index(factor)] = np.array(copy.copy(message)) #put the message in the "queue"
#move messages from incoming_messages to states
for var in incoming_messages:
states[var] = copy.copy(incoming_messages[var])
for vect in states[var]: #normalize the vectors
vect = (1.0/sum(vect))*vect
#our final states are just a product of all of the state vectors for each var
distributions = {}
for var in states:
dist = np.array([1 for _ in xrange(len(BN.vars[var]))])
for vect in states[var]:
dist = dist * vect
distributions[var] = [x/float(sum(dist)) for x in dist]
#write our results to a file
f = open(output, "w")
for var in BN.vars_in_order:
f.write("{} ".format(var))
for n in distributions[var]:
f.write("{} ".format(n))
f.write("\n")
f.close()
if __name__== '__main__':
net_name = sys.argv[1]
net = BNet(net_name)
sum_product(net, output = "{}-results.txt".format(net_name.replace(".bif", "")))
