# 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)
aimeanarray.append(aimean)
aistdarray.append(np.sqrt(aivar))
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)
aimean, aivar = ai.node_stats(beliefs, lmd=trunclmd)
aimeanarray.append(aimean)
aistdarray.append(np.sqrt(aivar))
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]
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]))
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)
belief = beliefs[0]
print 'Given an abnormal X-ray and a visit to Asia,'
print ' the probability of tuberculosis is {:f}\n'.format(belief)
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)
belief = beliefs[0]
print 'Given an abnormal X-ray and a visit to Asia,'
print ' the probability of tuberculosis is {:f}\n'.format(belief)
# add nodes to network
dbnet.add_nodes([r5, r4, m5, m4, e])
# 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("Straub2010Ex1.dne")
# prior belief
beliefs = dbnet.get_node_beliefs(e)
print "Prior Belief:"
for i in xrange(e.nstates()):
print 'The probability of {} is {:f}'.format(e.statenames[i], beliefs[i])
# posterior belief 1
m4measure = 50; m5measure=100
m4state = np.searchsorted(m4.bins, m4measure)-1
if m4state<0: m4state=0
m5state = np.searchsorted(m5.bins, m5measure)-1
if m5state<0: m5state=0
dbnet.enter_finding(m4, m4state)
dbnet.enter_finding(m5, m5state)
beliefs = dbnet.get_node_beliefs(e)
print "\nPosterior Belief:"
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]
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]))
# 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)
x2stats = x2.node_stats(x2beliefs, lmd=lmd)
print 'The mean of {} is {:f}'.format(x2.name, x2stats[0])
print 'The std of {} is {:f}'.format(x2.name, np.sqrt(x2stats[1]))
# add nodes to network
dbnet.add_nodes([r5, r4, m5, m4, e])
# 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("Straub2010Ex1.dne")
# prior belief
beliefs = dbnet.get_node_beliefs(e)
print "Prior Belief:"
for i in xrange(e.nstates()):
print 'The probability of {} is {:f}'.format(e.statenames[i],
beliefs[i])
# posterior belief 1
m4measure = 50
m5measure = 100
m4state = np.searchsorted(m4.bins, m4measure) - 1
if m4state < 0: m4state = 0
m5state = np.searchsorted(m5.bins, m5measure) - 1
if m5state < 0: m5state = 0
dbnet.enter_finding(m4, m4state)
dbnet.enter_finding(m5, m5state)
beliefs = dbnet.get_node_beliefs(e)
