def create_node_mi(name, parents, minum, milb, miub, mibins):
node_mi = Node(name, parents=parents, rvname='continuous')
minames = node_mi.discretize(milb, miub, minum, infinity='+-', bins=mibins)
node_insp = parents[0]
node_ai = parents[1]
ainum = node_ai.nstates()
labels = itertools.product(np.arange(node_insp.nstates()), np.arange(ainum))
labels = [label for label in labels]
for i,label in enumerate(labels):
if label[0] == 0: #no inspection
probs = 1./minum * np.ones(minum)
else: # with insepction
if label[0] == 1:
lmd = lmd1; beta = beta1
elif label[0] == 2:
lmd = lmd2; beta = beta2
elif label[0] == 3:
lmd = lmd3; beta = beta3
rvnames = ['Ai']
rvs = [node_ai.truncate_rv(label[1], lmd=trunclmd)]
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])
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)
# print 'labels: {}, progress: {}%, prob: {}'.format(label,
# float(i)/len(labels)*100, np.array_str(probs,precision=3))
return node_mi
def create_node_a(name,
parents,
ainum,
ailb,
aiub,
aiedges,
node_repair=None,
asmp0=None):
if node_repair is None:
node_ai = Node(name, parents=parents, rvname='continuous')
else:
node_ai = Node(name,
parents=parents + [node_repair],
rvname='continuous')
# dynamic discretization of nodes a and M
node_ap = parents[0]
knum = parents[1].nstates()
mnum = parents[2].nstates()
ainames = node_ai.discretize(ailb, aiub, ainum, infinity='+', bins=aiedges)
aibins = node_ai.bins
if node_repair is None:
labels = itertools.product(np.arange(node_ap.nstates()),
np.arange(knum), np.arange(mnum))
else:
labels = itertools.product(np.arange(node_ap.nstates()),
np.arange(knum), np.arange(mnum),
np.arange(2))
labels = [label for label in labels]
for i, label in enumerate(labels):
if len(label) == 4 and label[-1] == 1:
binnum, dummy = np.histogram(asmp0, aibins)
probs = binnum / np.sum(binnum, dtype=float)
node_ai.assign_cpt(probs,
label=np.asarray(label),
statenames=node_ai.statenames)
else:
truncrvs = []
for j, pstate in enumerate(label):
truncrvs.append(node_ai.parents[j].truncate_rv(pstate,
lmd=trunclmd))
rvnames = ['Ap', 'K', 'M']
rvs = truncrvs[:3]
probs, smpdb = mc2ai(rvnames, rvs, node_ai.bins, acrit, nsmp=nsmp)
# clean Ai states given Ai-1
apstate = label[0]
aplb = node_ap.bins[apstate]
aiubs = node_ai.bins[1:]
probs[aiubs <= aplb] = 0.
probs = probs / np.sum(probs)
node_ai.assign_cpt(probs,
label=np.asarray(label),
statenames=node_ai.statenames)
# print 'labels: {}, progress: {}%, prob: {}'.format(label,
# float(i)/len(labels)*100, np.array_str(probs,precision=3))
return node_ai
def create_node_a(name, parents, ainum, ailb, aiub, aiedges, node_repair=None, asmp0=None):
if node_repair is None:
node_ai = Node(name, parents=parents, rvname='continuous')
else:
node_ai = Node(name, parents=parents+[node_repair], rvname='continuous')
# dynamic discretization of nodes a and M
node_ap = parents[0]
knum = parents[1].nstates()
mnum = parents[2].nstates()
ainames = node_ai.discretize(ailb, aiub, ainum, infinity='+', bins=aiedges)
aibins = node_ai.bins
if node_repair is None:
labels = itertools.product(np.arange(node_ap.nstates()), np.arange(knum),np.arange(mnum))
else:
labels = itertools.product(np.arange(node_ap.nstates()), np.arange(knum),
np.arange(mnum), np.arange(2))
labels = [label for label in labels]
for i,label in enumerate(labels):
if len(label)==4 and label[-1] == 1:
binnum,dummy = np.histogram(asmp0, aibins)
probs = binnum/np.sum(binnum, dtype=float)
node_ai.assign_cpt(probs,label=np.asarray(label),statenames=node_ai.statenames)
else:
truncrvs=[]
for j,pstate in enumerate(label):
truncrvs.append(node_ai.parents[j].truncate_rv(pstate,lmd=trunclmd))
rvnames = ['Ap', 'K', 'M']
rvs = truncrvs[:3]
probs,smpdb = mc2ai(rvnames, rvs, node_ai.bins, acrit, nsmp=nsmp)
# clean Ai states given Ai-1
apstate = label[0]
aplb = node_ap.bins[apstate]
aiubs = node_ai.bins[1:]
probs[aiubs<=aplb] = 0.
probs = probs/np.sum(probs)
node_ai.assign_cpt(probs,label=np.asarray(label),statenames=node_ai.statenames)
# print 'labels: {}, progress: {}%, prob: {}'.format(label,
# float(i)/len(labels)*100, np.array_str(probs,precision=3))
return node_ai
def create_node_mi(name, parents, minum, milb, miub, mibins):
node_mi = Node(name, parents=parents, rvname='continuous')
minames = node_mi.discretize(milb, miub, minum, infinity='+-', bins=mibins)
node_insp = parents[0]
node_ai = parents[1]
ainum = node_ai.nstates()
labels = itertools.product(np.arange(node_insp.nstates()),
np.arange(ainum))
labels = [label for label in labels]
for i, label in enumerate(labels):
if label[0] == 0: #no inspection
probs = 1. / minum * np.ones(minum)
else: # with insepction
if label[0] == 1:
lmd = lmd1
beta = beta1
elif label[0] == 2:
lmd = lmd2
beta = beta2
elif label[0] == 3:
lmd = lmd3
beta = beta3
rvnames = ['Ai']
rvs = [node_ai.truncate_rv(label[1], lmd=trunclmd)]
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])
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)
# print 'labels: {}, progress: {}%, prob: {}'.format(label,
# float(i)/len(labels)*100, np.array_str(probs,precision=3))
return node_mi
ai = Node("A" + str(i + 1),
parents=[aarray[-1], node_k, node_m],
rvname='continuous')
mi = Node("M" + str(i + 1), parents=[ai], rvname='continuous')
aarray.append(ai)
marray.append(mi)
# discretize continuous rv
# node a0
a0num = 20 + 2
mu, var = rv_a0.stats()
sigma = np.sqrt(var)
lb = mu - 3. * sigma
ub = mu + 3. * sigma
a0bins = np.linspace(lb, ub, a0num - 1)
a0names = node_a0.discretize(lb, ub, a0num, infinity='+-', bins=a0bins)
# node m
mnum = 20 + 2
mu, var = rv_m.stats()
sigma = np.sqrt(var)
lb = mu - 3. * sigma
ub = mu + 3. * sigma
mbins = np.linspace(lb, ub, mnum - 1)
mnames = node_m.discretize(lb, ub, mnum, infinity='+-', bins=mbins)
# node k
knum = 20 + 1
ksmp_prior, msmp_prior = ksmp_mc(nsmp, rv_C, rv_Sre, G, rv_m, rv_Na)
klb = np.percentile(ksmp_prior, 5)
kub = np.percentile(ksmp_prior, 95)
if klb > 0:
kbins = np.hstack((0., np.linspace(klb, kub, knum - 1)))
node_a0 = Node('a0', parents=None, rvname='normal', rv=rv_a0)
aarray = [node_a0]
marray=[]
for i in range(life):
ai = Node("A"+str(i+1), parents=[aarray[-1], node_k, node_m], rvname='continuous')
mi = Node("M"+str(i+1), parents=[ai], rvname='continuous')
aarray.append(ai)
marray.append(mi)
# discretize continuous rv
# node a0
a0num = 20+2
mu,var = rv_a0.stats(); sigma = np.sqrt(var)
lb = mu-3.*sigma; ub = mu+3.*sigma
a0bins = np.linspace(lb, ub, a0num-1)
a0names = node_a0.discretize(lb, ub, a0num, infinity='+-', bins=a0bins)
# node m
mnum = 20+2
mu,var = rv_m.stats(); sigma = np.sqrt(var)
lb = mu-3.*sigma; ub = mu+3.*sigma
mbins = np.linspace(lb, ub, mnum-1)
mnames = node_m.discretize(lb, ub, mnum, infinity='+-', bins=mbins)
# node k
knum = 20+1
ksmp_prior,msmp_prior = ksmp_mc(nsmp, rv_C, rv_Sre, G, rv_m, rv_Na)
klb = np.percentile(ksmp_prior, 5)
kub = np.percentile(ksmp_prior, 95)
if klb>0:
kbins = np.hstack((0., np.linspace(klb, kub, knum-1)))
else:
kbins = np.linspace(0, kub, knum)
for i in range(life):
h = Node("H"+str(i+1), parents=[uh], rvname='continuous')
e = Node("E"+str(i+1),parents=[earray[-1],q,h], rvname='discrete')
harray.append(h)
earray.append(e)
# discretize continuous rv
# r4, m4, r5 and m5
r4num = 20+1
r5num = 20+1
m4num = 20+2
m5num = 20+2
m = r5.rv.stats('m'); s = np.sqrt(r5.rv.stats('v'))
lb = 50.; ub = 250.
r4bins = np.hstack((0, np.linspace(lb, ub, r4num-1)))
r4names = r4.discretize(lb, ub, r4num, infinity='+', bins=r4bins)
m4names = m4.discretize(lb, ub, m4num, infinity='+-', bins=r4bins)
lb = 50.; ub = 250.
r5bins = np.hstack((0, np.linspace(lb, ub, r5num-1)))
r5names = r5.discretize(lb, ub, r5num, infinity='+', bins=r5bins)
m5names = m5.discretize(lb, ub, m5num, infinity='+-', bins=r5bins)
# q
qnum = 30+1
qlb = 0.; qub = 150.
qbins = np.hstack(np.linspace(qlb, qub, qnum))
qnames = q.discretize(qlb, qub, qnum, infinity='+', bins=qbins)
# uh
uhnum = 50+1
uhlb = 0.; uhub = 150.
uhbins = np.hstack(np.linspace(uhlb, uhub, uhnum))
uhnames = uh.discretize(uhlb, uhub, uhnum, infinity='+', bins=uhbins)
# create nodes
r5 = Node("R5", parents=None, rvname='lognormal', rv=rv5)
r4 = Node("R4", parents=[r5], rvname='lognormal', rv=rv4)
m4 = Node("M4", parents=[r4], rvname='continuous')
m5 = Node("M5", parents=[r5], rvname='continuous')
e = Node("E",parents=[r4,r5], rvname='discrete')
# discretize continuous rv
r4num = 20+1
r5num = 20+1
m4num = 20+2
m5num = 20+2
m = r5.rv.stats('m'); s = np.sqrt(r5.rv.stats('v'))
lb = 50.; ub = 250.
r4bins = np.hstack((0, np.linspace(lb, ub, r4num-1)))
r4names = r4.discretize(lb, ub, r4num, infinity='+', bins=r4bins)
m4names = m4.discretize(lb, ub, m4num, infinity='+-', bins=r4bins)
lb = 50.; ub = 250.
r5bins = np.hstack((0, np.linspace(lb, ub, r5num-1)))
r5names = r5.discretize(lb, ub, r5num, infinity='+', bins=r5bins)
m5names = m5.discretize(lb, ub, m5num, infinity='+-', bins=r5bins)
# calculate and assignCPT
# node R5
r5cpt = r5.rv.cdf(r5.bins[1:]) - r5.rv.cdf(r5.bins[:-1])
r5cpt = r5cpt[np.newaxis,:]
r5.assign_cpt(r5cpt, statenames=r5names)
# node R4
nstate = r4num
labels = itertools.product(np.arange(r5.nstates()))
mvnorm = stats.multivariate_normal(mean=None,cov=np.array([[1.,rolnR],[rolnR,1.]]))
rvname='discrete')
harray.append(h)
earray.append(e)
# discretize continuous rv
# r4, m4, r5 and m5
r4num = 20 + 1
r5num = 20 + 1
m4num = 20 + 2
m5num = 20 + 2
m = r5.rv.stats('m')
s = np.sqrt(r5.rv.stats('v'))
lb = 50.
ub = 250.
r4bins = np.hstack((0, np.linspace(lb, ub, r4num - 1)))
r4names = r4.discretize(lb, ub, r4num, infinity='+', bins=r4bins)
m4names = m4.discretize(lb, ub, m4num, infinity='+-', bins=r4bins)
lb = 50.
ub = 250.
r5bins = np.hstack((0, np.linspace(lb, ub, r5num - 1)))
r5names = r5.discretize(lb, ub, r5num, infinity='+', bins=r5bins)
m5names = m5.discretize(lb, ub, m5num, infinity='+-', bins=r5bins)
# q
qnum = 30 + 1
qlb = 0.
qub = 150.
qbins = np.hstack(np.linspace(qlb, qub, qnum))
qnames = q.discretize(qlb, qub, qnum, infinity='+', bins=qbins)
# uh
uhnum = 50 + 1
uhlb = 0.
# random variables
rvX1 = stats.lognorm(1., scale=np.exp(0))
rvX3 = stats.lognorm(1., scale=np.exp(3*np.sqrt(2)))
# create nodes
x1 = Node("X1", parents=None, rvname='lognormal', rv=rvX1)
x2 = Node("X2", parents=[x1], rvname='continuous')
y = Node("Y", parents=[x2], rvname='discrete')
# discretize continuous rv
x1num = 5
x2num = 5
m = x1.rv.stats('m'); s = np.sqrt(x1.rv.stats('v'))
#lb = np.maximum(0, m-2*s); ub = m+2*s
lb = 0.; ub = m+1.5*s
x1names = x1.discretize(lb, ub, x1num, infinity='+')
x2names = x2.discretize(lb*10., ub*10., x2num, infinity='+')
# calculate and assign CPT
# node X1
x1cpt = x1.rv.cdf(x1.bins[1:]) - x1.rv.cdf(x1.bins[:-1])
x1cpt = x1cpt[np.newaxis,:]
x1.assign_cpt(x1cpt, statenames=x1names)
# node X2
lmd = 0.05
nstate = x2num
labels = itertools.product(np.arange(x1.nstates()))
for i, label in enumerate(labels):
rvs = []
probs=[]
for j, pstate in enumerate(label):
r4 = Node("R4", parents=[r5], rvname='lognormal', rv=rv4)
m4 = Node("M4", parents=[r4], rvname='continuous')
m5 = Node("M5", parents=[r5], rvname='continuous')
e = Node("E", parents=[r4, r5], rvname='discrete')
# discretize continuous rv
r4num = 20 + 1
r5num = 20 + 1
m4num = 20 + 2
m5num = 20 + 2
m = r5.rv.stats('m')
s = np.sqrt(r5.rv.stats('v'))
lb = 50.
ub = 250.
r4bins = np.hstack((0, np.linspace(lb, ub, r4num - 1)))
r4names = r4.discretize(lb, ub, r4num, infinity='+', bins=r4bins)
m4names = m4.discretize(lb, ub, m4num, infinity='+-', bins=r4bins)
lb = 50.
ub = 250.
r5bins = np.hstack((0, np.linspace(lb, ub, r5num - 1)))
r5names = r5.discretize(lb, ub, r5num, infinity='+', bins=r5bins)
m5names = m5.discretize(lb, ub, m5num, infinity='+-', bins=r5bins)
# calculate and assignCPT
# node R5
r5cpt = r5.rv.cdf(r5.bins[1:]) - r5.rv.cdf(r5.bins[:-1])
r5cpt = r5cpt[np.newaxis, :]
r5.assign_cpt(r5cpt, statenames=r5names)
# node R4
nstate = r4num
labels = itertools.product(np.arange(r5.nstates()))
