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))
# 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:"
for i in xrange(e.nstates()):
print 'Given m4={:.1f} and m5={:.1f}, the probability of {} is {:f}'.format(m4measure, m5measure, e.statenames[i], beliefs[i])
r4beliefs = dbnet.get_node_beliefs(r4)
r5beliefs = dbnet.get_node_beliefs(r5)
r4rvs = [r4.truncate_rv(pstate) for pstate in np.arange(r4.nstates())]
r5rvs = [r5.truncate_rv(pstate) for pstate in np.arange(r5.nstates())]
np.savez('bndiscrete10.npz', r4beliefs=r4beliefs, r5beliefs=r5beliefs,
r4bins=r4.bins, r5bins=r5.bins, r4rvs=r4rvs, r5rvs=r5rvs)
# posterior belief 2
dbnet.retract_nodefindings(m4)
#dbnet.retract_netfindings()
m4measure = 150; m5measure=100
m4state = np.searchsorted(m4.bins, m4measure)-1
if m4state<0: m4state=0
dbnet.enter_finding(m4, m4state)
#m5state = np.searchsorted(m5.bins, m5measure)-1
#if m5state<0: m5state=0
#dbnet.enter_finding(m5, m5state)
beliefs = dbnet.get_node_beliefs(e)
# 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))
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:"
for i in xrange(e.nstates()):
print 'Given m4={:.1f} and m5={:.1f}, the probability of {} is {:f}'.format(
m4measure, m5measure, e.statenames[i], beliefs[i])
r4beliefs = dbnet.get_node_beliefs(r4)
r5beliefs = dbnet.get_node_beliefs(r5)
r4rvs = [r4.truncate_rv(pstate) for pstate in np.arange(r4.nstates())]
r5rvs = [r5.truncate_rv(pstate) for pstate in np.arange(r5.nstates())]
np.savez('bndiscrete10.npz',
r4beliefs=r4beliefs,
r5beliefs=r5beliefs,
r4bins=r4.bins,
r5bins=r5.bins,
r4rvs=r4rvs,
r5rvs=r5rvs)
# posterior belief 2
dbnet.retract_nodefindings(m4)
#dbnet.retract_netfindings()
m4measure = 150
m5measure = 100
m4state = np.searchsorted(m4.bins, m4measure) - 1