rvs = truncrvs
aimean = rvs[0].stats('m')[()]
rv_am = stats.norm(aimean, sigmae)
pod = 1.-stats.norm.cdf((np.log(aimean)-lmd)/beta)
probs = rv_am.cdf(node_mi.bins[1:])-rv_am.cdf(node_mi.bins[:-1])
print 'labels: {}, progress: {}%, prob: {}'.format(label,
float(i)/len(labels)*100, np.array_str(probs,precision=3))
probs = probs/np.sum(probs)*pod
probs[0] = probs[0]+(1.-pod)
node_mi.assign_cpt(probs,label=np.asarray(label),statenames=node_mi.statenames)
# 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:
pod = 1. - stats.norm.cdf((np.log(aimean) - lmd2) / beta2)
probs = rv_am.cdf(node_mi.bins[1:]) - rv_am.cdf(node_mi.bins[:-1])
print 'labels: {}, progress: {}%, prob: {}'.format(
label,
float(i) / len(labels) * 100, np.array_str(probs, precision=3))
probs = probs / np.sum(probs) * pod
probs[0] = probs[0] + (1. - pod)
node_mi.assign_cpt(probs,
label=np.asarray(label),
statenames=node_mi.statenames)
# 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:
for j,pstate in enumerate(label[1:]):
truncrvs.append(e.parents[j+1].truncate_rv(pstate))
if label[0] == 1: # failure in previous time slot
prob = 1.
else:
prob = intg2e(truncrvs[0], truncrvs[1])
print 'labels: {}, progress: {}%, prob: {}'.format(label,
float(i)/len(labels)*100, prob)
probs = np.array([1.-prob, prob])
e.assign_cpt(probs,label=np.asarray(label),statenames=['safe','fail'], labels=labels)
# 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 = []
# assign CPT
visitAsiaCpt = np.array([0.01, 0.99])[np.newaxis, :]
visitAsia.assign_cpt(visitAsiaCpt, statenames=['visit', 'no_visit'])
smokingCpt = np.array([0.50, 0.50])[np.newaxis, :]
smoking.assign_cpt(smokingCpt, statenames=['smoker', 'nonsmoker'])
tuberCpt = np.array([[0.05, 0.95], [0.01, 0.99]])
tuberculosis.assign_cpt(tuberCpt, statenames=['present', 'absent'])
cancerCpt = np.array([[0.10, 0.90], [0.01, 0.99]])
cancer.assign_cpt(cancerCpt, statenames=['present', 'absent'])
tbOrCaCpt = np.array([[1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [0.0, 1.0]])
tbOrCa.assign_cpt(tbOrCaCpt, statenames=['true', 'false'])
xRayCpt = np.array([[0.98, 0.02], [0.05, 0.95]])
xRay.assign_cpt(xRayCpt, statenames=['abnormal', 'normal'])
# add node
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')
for j,pstate in enumerate(label):
truncrvs.append(e.parents[j].truncate_rv(pstate))
rvnames = ['ur', 'u1', 'u2', 'u3', 'u4', 'u5', 'h', 'v']
rvs = [ur, u1, u2, u3, u4, u5, h, v]
trunclb = [rv.lb for rv in truncrvs]
truncub = [rv.ub for rv in truncrvs]
syspf = msr2e(rvnames, rvs, logmean, logstd, rolnR, trunclb, truncub)
probs = np.array([1.-syspf, syspf])
e.assign_cpt(probs,label=np.asarray(label),statenames=['safe', 'fail'])
print 'labels: {}, progress: {}%, pf: {}'.format(label, float(i)/len(labels)*100, syspf)
# create new network
dbnet = Network("Straub2010Ex1")
# 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:"
# assign CPT
visitAsiaCpt = np.array([0.01, 0.99])[np.newaxis, :]
visitAsia.assign_cpt(visitAsiaCpt, statenames=['visit','no_visit'])
smokingCpt = np.array([0.50, 0.50])[np.newaxis, :]
smoking.assign_cpt(smokingCpt, statenames=['smoker','nonsmoker'])
tuberCpt = np.array([[0.05, 0.95], [0.01, 0.99]])
tuberculosis.assign_cpt(tuberCpt, statenames=['present','absent'])
cancerCpt = np.array([[0.10, 0.90], [0.01, 0.99]])
cancer.assign_cpt(cancerCpt, statenames=['present','absent'])
tbOrCaCpt = np.array([[1.0, 0.0], [1.0, 0.0], [1.0, 0.0], [0.0, 1.0]])
tbOrCa.assign_cpt(tbOrCaCpt, statenames=['true','false'])
xRayCpt = np.array([[0.98, 0.02], [0.05, 0.95]])
xRay.assign_cpt(xRayCpt, statenames=['abnormal','normal'])
# add node
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')
def calculate_util(pstate):
forecaststate,umbrellastate = pstate
if forecaststate==0 and umbrellastate==0:
return 20.
elif forecaststate==0 and umbrellastate==1:
return 100.
elif forecaststate==1 and umbrellastate==0:
return 70.
elif forecaststate==1 and umbrellastate==1:
return 0.
satisfy.assign_func(calculate_util)
# 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
if aistate < acrstate:
utilr = 0.
elif aistate > acrstate:
utilr = -Cfail
elif aistate == acrstate:
pf = 1. - truncrv_ai.cdf(acrit)
utilr = -pf * Cfail
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)
else:
prob = intg2e(truncrvs[0], truncrvs[1])
print 'labels: {}, progress: {}%, prob: {}'.format(
label,
float(i) / len(labels) * 100, prob)
probs = np.array([1. - prob, prob])
e.assign_cpt(probs,
label=np.asarray(label),
statenames=['safe', 'fail'],
labels=labels)
# 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 = []
utilr = 0.
elif aistate>acrstate:
utilr = -Cfail
elif aistate == acrstate:
pf = 1.-truncrv_ai.cdf(acrit)
utilr = -pf*Cfail
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()
corr = np.eye(nstate)
for i, label in enumerate(labels):
rvs = []
probs=[]
for j, pstate in enumerate(label):
rvs.append(y.parents[j].truncate_rv(pstate, lmd=lmd))
rvs.append(rvX3)
pf = form2y(rvnames, rvs, corr)
probs = np.array([1.-pf, pf])
y.assign_cpt(probs,label=np.asarray(label),statenames=['neg', 'init'])
# 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:"
trunclb = [rv.lb for rv in truncrvs]
truncub = [rv.ub for rv in truncrvs]
syspf = msr2e(rvnames, rvs, logmean, logstd, rolnR, trunclb, truncub)
probs = np.array([1. - syspf, syspf])
e.assign_cpt(probs,
label=np.asarray(label),
statenames=['safe', 'fail'])
print 'labels: {}, progress: {}%, pf: {}'.format(
label,
float(i) / len(labels) * 100, syspf)
# create new network
dbnet = Network("Straub2010Ex1")
# 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:"
if forecaststate == 0 and umbrellastate == 0:
return 20.
elif forecaststate == 0 and umbrellastate == 1:
return 100.
elif forecaststate == 1 and umbrellastate == 0:
return 70.
elif forecaststate == 1 and umbrellastate == 1:
return 0.
satisfy.assign_func(calculate_util)
# 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)
