def or_d(x, y):
out = defaultdict(lambdaMinusInfinity)
out[True] = logplusexp(
x.get(True, -Infinity) + y.get(False, -Infinity),
x.get(False, -Infinity) + y.get(True, -Infinity))
out[False] = log1mexp(out[True])
return out
def cons_d(x, y):
out = defaultdict(lambdaMinusInfinity)
for a, av in list(x.items()):
for b, bv in list(y.items()):
out[a + b] = logplusexp(out[a + b], av + bv)
return out
def if_d(prb, x, y):
out = defaultdict(lambdaMinusInfinity)
pt = prb[True]
pf = prb[False]
for a, av in list(x.items()):
out[a] = av + pt
for b, bv in list(y.items()):
out[b] = logplusexp(out[b], bv + pf)
return out
def compute_outcomes(f, *args, **kwargs):
"""
Return a dictionary of outcomes using our RandomContext tools, giving each possible trace (up to the given depth)
and its probability.
f here is a function of context, as in f(context, *args)
kwargs['Cfirst'] constrols whether C is the first or last argument to f. It cannot be anything else
In kwargs you can pass "catchandpass" as a tuple of exceptions to catch and do nothing with
"""
out = defaultdict(lambdaMinusInfinity) # dict from strings to lps that we accumulate
cs = ContextSet() # this is the "open" set of contexts we need to explore
cs.add(RandomContext(cs)) # add a single context with no history
i = 0
while len(cs) > 0:
context = cs.pop() # pop an element from Context set.
# print "CTX", context.lp, context#, " \t", cs.Q
try:
# figure out the ordering of where C is passed to the lambda
if kwargs.get('Cfirst', True):# does C go at the beginning or the end?
v = f(context, *args) # when we call context.flip, we may update cs with new paths to explore
else:
newargs = args + (context,)
v = f(*newargs)
# print ">>>", v
# add up the lp for this outcomem
out[v] = logplusexp(out[v], context.lp)
except kwargs.get('catchandpass', None) as e:
pass
except ContextSizeException: # prune that path
pass
if i >= kwargs.get('maxit', 1000):
return out ## TODO: Hmm can either return the partial answer here or raise an exception
if len(cs) > kwargs.get('maxcontext', 1000): # sometimes we can generate way too many contexts, so let's avoid that
raise TooManyContextsException
i += 1
return out
def equal_d(x, y):
peq = -Infinity
for a, v in list(x.items()):
peq = logplusexp(peq, v + y.get(a, -Infinity)) # P(x=a,y=a)
return {True: peq, False: log1mexp(peq)}
def car_d(x):
out = defaultdict(lambdaMinusInfinity)
for a, av in list(x.items()):
v = a[1] if len(a) > 1 else ''
out[v] = logplusexp(out[v], av)
return out