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
aiub,
minum,
infinity='+-',
bins=aiedges)
nstate = ainum
labels = itertools.product(np.arange(node_ap.nstates()),
np.arange(knum), np.arange(mnum))
labels = [label for label in labels]
for i, label in enumerate(labels):
truncrvs = []
for j, pstate in enumerate(label):
truncrvs.append(node_ai.parents[j].truncate_rv(pstate,
lmd=trunclmd))
rvnames = ['Ap', 'K', 'M']
rvs = truncrvs
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)
print 'labels: {}, progress: {}%, prob: {}'.format(
label,
float(i) / len(labels) * 100, np.array_str(probs, precision=3))
node_ai.assign_cpt(probs,
label=np.asarray(label),
statenames=node_ai.statenames)
# node mi
nstate = minum
labels = itertools.product(np.arange(ainum))
else:
aibins = np.linspace(0., aiub, ainum-1)
aibins = np.hstack((aibins,acrit+1e-3))
ainames = node_ai.discretize(ailb, aiub, ainum, infinity='+', bins=aibins)
minames = node_mi.discretize(ailb, aiub, minum, infinity='+-', bins=aibins)
nstate = ainum
# ai = Node("A"+str(i+1), parents=[aarray[-1], node_k, node_m], rvname='continuous')
labels = itertools.product(np.arange(node_ap.nstates()), np.arange(knum),np.arange(mnum))
labels = [label for label in labels]
for i,label in enumerate(labels):
truncrvs=[]
for j,pstate in enumerate(label):
truncrvs.append(node_ai.parents[j].truncate_rv(pstate,lmd=trunclmd))
rvnames = ['Ap', 'K', 'M']
rvs = truncrvs
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)
print 'labels: {}, progress: {}%, prob: {}'.format(label,
float(i)/len(labels)*100, np.array_str(probs,precision=3))
node_ai.assign_cpt(probs,label=np.asarray(label),statenames=node_ai.statenames)
# node mi
nstate = minum
labels = itertools.product(np.arange(ainum))
labels = [label for label in labels]
for i,label in enumerate(labels):
truncrvs=[]
