# 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))
# 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]))
optx, dummy = x2.fit_stats('lognormal', x2beliefs, lmd=lmd)
print 'The mean of {} by fitting cdf is {:f}'.format(x2.name, optx[0])
print 'The std of {} by fitting cdf is {:f}'.format(x2.name, np.sqrt(optx[1]))
# posterior belief
dbnet.enter_finding(x1, 2)
beliefs = dbnet.get_node_beliefs(y)
