print "Evidence 1: survive up to year 5, h5=80"
for e in earray[1:6]:
dbnet.enter_finding(e,0)
h5ev = 80
h5 = harray[4]
h5state = np.searchsorted(h5.bins, h5ev)-1
dbnet.enter_finding(h5, h5state)
beta1 = []
for e in earray[1:]:
beliefs = dbnet.get_node_beliefs(e)
beta1.append(-stats.norm.ppf(beliefs[1]))
uhbeliefs1 = dbnet.get_node_beliefs(uh)
# evidence 2: survive up to year 5, hi = 30, h5=80
dbnet.retract_netfindings()
print "Evidence 2: survive up to year 5, hi=30, h5=80"
for e in earray[1:6]:
dbnet.enter_finding(e,0)
hiev=30
for h in harray[:4]:
histate = np.searchsorted(h.bins, hiev)-1
dbnet.enter_finding(h,histate)
h5ev = 80
h5 = harray[4]
h5state = np.searchsorted(h5.bins, h5ev)-1
dbnet.enter_finding(h5, h5state)
beta2 = []
for e in earray[1:]:
beliefs = dbnet.get_node_beliefs(e)
beta2.append(-stats.norm.ppf(beliefs[1]))
return utilr
node_fr.assign_func(failure_risk)
# create new network
dbnet = Network("Soliman2014InfDiag")
# add nodes to network
dbnet.add_nodes([node_m, node_k] + aarray + marray + insparray + uiarray +
rarray + urarray + [node_fr])
# add link: must be prior to defining nodes
dbnet.add_link()
# define nodes
dbnet.define_nodes()
# compile the net
dbnet.compile_net()
# enable autoupdate
dbnet.set_autoupdate()
# save the network
dbnet.save_net("Soliman2014InfDiag.dne")
#type 2
dbnet.retract_netfindings()
dummy = dbnet.get_node_expectedutils(node_insp)
dummy = dbnet.get_node_expectedutils(node_repair)
decision = dbnet.get_node_funcstate(node_repair, [2, 4])
print 'If the inspection decision is {} and meausre is {}, the best repair decision is {}.\n'.format(
node_insp.statenames[2], marray[0].statenames[4],
node_repair.statenames[decision])
