# create new network
dbnet = Network("Soliman2014DBN")
# add nodes to network
dbnet.add_nodes([node_m, node_k]+aarray+marray)
# 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("Soliman2014DBN.dne")
# inferences
pfarray = []
for ai in aarray:
beliefs = dbnet.get_node_beliefs(ai)
aistate = np.searchsorted(ai.bins, acrit)-1
aitruncrv = ai.truncate_rv(aistate,lmd=trunclmd)
lt = np.sum(beliefs[:aistate])+beliefs[aistate]*aitruncrv.cdf(acrit)
pfarray.append(1-lt)
aimeanarray = []
aistdarray = []
for ai in aarray:
beliefs = dbnet.get_node_beliefs(ai)
aimean,aivar = ai.node_stats(beliefs,lmd=trunclmd)
# create new network
dbnet = Network("Soliman2014DBN")
# add nodes to network
dbnet.add_nodes([node_m, node_k] + aarray + marray)
# 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("Soliman2014DBN.dne")
# inferences
pfarray = []
for ai in aarray:
beliefs = dbnet.get_node_beliefs(ai)
aistate = np.searchsorted(ai.bins, acrit + 1e-10) - 1
aitruncrv = ai.truncate_rv(aistate, lmd=trunclmd)
lt = np.sum(
beliefs[:aistate]) + beliefs[aistate] * aitruncrv.cdf(acrit)
pfarray.append(1 - lt)
aimeanarray = []
aistdarray = []
for ai in aarray:
beliefs = dbnet.get_node_beliefs(ai)
# create new network
dbnet = Network("Straub2010Ex2")
# add nodes to network
dbnet.add_nodes([r5, r4, m5, m4, uh, q]+harray+earray)
# 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("Straub2010Ex2.dne")
# no evidence
print "Prior Belief:"
beta0 = []
for e in earray[1:]:
beliefs = dbnet.get_node_beliefs(e)
beta0.append(-stats.norm.ppf(beliefs[1]))
uhbeliefs0 = dbnet.get_node_beliefs(uh)
# evidence 1: survive up to year 5, h5=80
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]
netp.add_nodes([visitAsia])
netp.add_nodes([smoking])
netp.add_nodes([tuberculosis])
netp.add_nodes([cancer])
netp.add_nodes([tbOrCa, xRay])
# add link: must before define nodes
netp.add_link()
# define nodes
netp.define_nodes()
# compile the net
netp.compile_net()
# enable auto updating
netp.set_autoupdate()
# save the network
netp.save_net('ChestClinicNew.dne')
# prior belief
beliefs = netp.get_node_beliefs(tuberculosis)
belief = beliefs[0]
print 'The probability of tuberculosis is {:f}\n'.format(belief)
# posterior belief
netp.enter_finding(xRay, 0)
beliefs = netp.get_node_beliefs(tuberculosis)
belief = beliefs[0]
print 'Given an abnormal X-ray,'
print ' the probability of tuberculosis is {:f}\n'.format(belief)
netp.enter_finding(visitAsia, 0)
beliefs = netp.get_node_beliefs(tuberculosis)
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])
netp.add_nodes([visitAsia])
netp.add_nodes([smoking])
netp.add_nodes([tuberculosis])
netp.add_nodes([cancer])
netp.add_nodes([tbOrCa, xRay])
# add link: must before define nodes
netp.add_link()
# define nodes
netp.define_nodes()
# compile the net
netp.compile_net()
# enable auto updating
netp.set_autoupdate()
# save the network
netp.save_net('ChestClinicNew.dne')
# prior belief
beliefs = netp.get_node_beliefs(tuberculosis)
belief = beliefs[0]
print 'The probability of tuberculosis is {:f}\n'.format(belief)
# posterior belief
netp.enter_finding(xRay, 0)
beliefs = netp.get_node_beliefs(tuberculosis)
belief = beliefs[0]
print 'Given an abnormal X-ray,'
print ' the probability of tuberculosis is {:f}\n'.format(belief)
netp.enter_finding(visitAsia, 0)
beliefs = netp.get_node_beliefs(tuberculosis)
# create new network
dbnet = Network("BNexample1")
# add nodes to network
dbnet.add_nodes([weather, forecast, umbrella, satisfy])
# add link: must before define 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("BNdecision.dne")
# decision making
# type 1
dbnet.enter_finding(forecast, 0)
utils = dbnet.get_node_expectedutils(umbrella)
print 'If the forecast is sunny, expected utility of {} is {}, of {} is {}\n'.format(
umbrella.statenames[0], utils[0],
umbrella.statenames[1], utils[1])
dbnet.retract_netfindings()
dbnet.enter_finding(forecast, 1)
utils = dbnet.get_node_expectedutils(umbrella)
print 'If the forecast is cloudy, expected utility of {} is {}, of {} is {}\n'.format(
umbrella.statenames[0], utils[0],
# create new network
dbnet = Network("Straub2010Ex2")
# add nodes to network
dbnet.add_nodes([r5, r4, m5, m4, uh, q] + harray + earray)
# 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("Straub2010Ex2.dne")
# no evidence
print "Prior Belief:"
beta0 = []
for e in earray[1:]:
beliefs = dbnet.get_node_beliefs(e)
beta0.append(-stats.norm.ppf(beliefs[1]))
uhbeliefs0 = dbnet.get_node_beliefs(uh)
# evidence 1: survive up to year 5, h5=80
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]
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])
# create new network
dbnet = Network("BNexample1")
# add nodes to network
dbnet.add_nodes([x1, x2, y])
# add link: must before define 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("BNexample1.dne")
# prior belief
beliefs = dbnet.get_node_beliefs(y)
print "Prior Belief:"
for i in xrange(y.nstates()):
print 'The probability of {} is {:f}'.format(y.statenames[i], beliefs[i])
x1beliefs = dbnet.get_node_beliefs(x1)
# statesvalues = x1.statenames.astype(float)
x1stats = x1.node_stats(x1beliefs)
print 'The mean of {} is {:f}'.format(x1.name, x1stats[0])
print 'The std of {} is {:f}'.format(x1.name, np.sqrt(x1stats[1]))
x2beliefs = dbnet.get_node_beliefs(x2)
# statesvalues = x2.statenames.astype(float)
# create new network
dbnet = Network("BNexample1")
# add nodes to network
dbnet.add_nodes([weather, forecast, umbrella, satisfy])
# add link: must before define 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("BNdecision.dne")
# decision making
# type 1
dbnet.enter_finding(forecast, 0)
utils = dbnet.get_node_expectedutils(umbrella)
print 'If the forecast is sunny, expected utility of {} is {}, of {} is {}\n'.format(
umbrella.statenames[0], utils[0], umbrella.statenames[1], utils[1])
dbnet.retract_netfindings()
dbnet.enter_finding(forecast, 1)
utils = dbnet.get_node_expectedutils(umbrella)
print 'If the forecast is cloudy, expected utility of {} is {}, of {} is {}\n'.format(
umbrella.statenames[0], utils[0], umbrella.statenames[1], utils[1])
dbnet.retract_netfindings()
