def setChannel(self,distribution):
cases = []
pvalues = []
for (names,pvalue) in distribution:
cases.append(self.name+"_"+names[0])
pvalues.append(pvalue)
domain = efp.Dom(cases,names = efp.Name(self.name))
self.channel = efp.State(pvalues,domain).as_chan()
self.node_size = len(self.channel.cod[0])
def setChannel(self,cpt):
codomainElements = OrderedSet([names[-1] for (names,_) in cpt])
NumCodElems = len(codomainElements)
codomain = efp.Dom([self.name+"_"+elem for elem in codomainElements],names = efp.Name(self.name))
doms = []
steps = 1
for (i,pn) in enumerate(self.parents):
domainElements = OrderedSet([names[i] for (names,_) in cpt])
doms.append(efp.Dom([pn.name+"_"+elem for elem in domainElements],names = efp.Name(pn.name)))
steps *= len(domainElements)
dom = functools.reduce(lambda d1, d2: d1 + d2, doms)
pvalues = [row for (_,row) in cpt]
matrix = [pvalues[n:n+NumCodElems] for n in range(0,len(pvalues),NumCodElems)]
states = [efp.State(matrix[j], codomain) for j in range(steps)]
self.channel = efp.chan_from_states(states, dom)
self.node_size = len(self.channel.cod[0])
def run(self, state=ep.State(1, [])):
s = self.chan >> state
s = s.partial_conditional(ep.yes_pred @ truth(self.cod))
return s % ([0] + [1] * len(self.cod))
def run(self, state=ep.State(1, [])):
return (self.chan >> state).normalize()
prob_choice(0.4, newassign(lambda: 0, range(2), []),
newassign(lambda: 1, range(2), [])),
observe(lambda x: x == 0, [range(2)])).run()
test2 = do(
prob_choice(
0.4,
do(newassign(lambda: 0, range(2), []),
observe(lambda x: x == 0, [range(2)])),
do(newassign(lambda: 1, range(2), []),
observe(lambda x: x == 0, [range(2)])))).run()
disease_dom = ['D', '~D']
mood_dom = ['M', '~M']
bool_dom = [True, False]
inital_state = ep.State([0.05, 0.5, 0.4, 0.05], [disease_dom, mood_dom])
test3 = do(
ifthenelse(
lambda d, m: d == 'D',
newsample(lambda *_: ep.flip(9 / 10), bool_dom,
[disease_dom, mood_dom]),
newsample(lambda *_: ep.flip(1 / 20), bool_dom,
[disease_dom, mood_dom])),
observe(lambda d, m, t: t == True, [disease_dom, mood_dom, bool_dom]),
discard(0, [disease_dom, mood_dom, bool_dom]),
discard(1, [mood_dom, bool_dom])).run(inital_state)
# Use prob_choice and assign instead