def condition(rvs, observations):
if len(rvs) > 1:
raise NotImplementedError()
observations = typed_items(observations)
# if none of the rvs show up in the ancestors of any observations
# then this is easy conditioning
obs_ancestors = ancestors(observations.keys(), blockers=rvs)
if any(rv in obs_ancestors for rv in rvs):
# not-so-easy conditioning
# we need to produce a sampler-driven model
raise NotImplementedError()
else:
# easy conditioning
rvs_anc = ancestors(rvs, blockers=observations.keys())
frontier = [r for r in rvs_anc
if r.owner is None or r in observations.keys()]
cloned_inputs, cloned_outputs = clone_keep_replacements(frontier, rvs,
replacements=observations)
return cloned_outputs
def full_log_likelihood(assignment):
"""
Return log(P(rv0=sample))
assignment: rv0=val0, rv1=val1, ...
Each of val0, val1, ... v0, v1, ... is supposed to represent an identical
number of draws from a distribution. This function returns the real-valued
density for each one of those draws.
The output from this function may be a random variable, if not all sources
of randomness are removed by the assignment and the given.
"""
for rv in assignment.keys():
if not is_rv(rv):
raise ValueError('non-random var in assignment key', rv)
# All random variables that are not assigned should stay as the same object so it can later be replaced
# If this is not done this way, they get cloned
RVs = [v for v in ancestors(assignment.keys()) if is_raw_rv(v)]
for rv in RVs:
if rv not in assignment:
assignment[rv] = rv
# Cast assignment elements to the right kind of thing
assignment = typed_items(assignment)
pdfs = [lpdf(rv, sample) for rv, sample in assignment.items()]
lik = tensor.add(*[tensor.sum(p) for p in pdfs])
dfs_variables = ancestors([lik], blockers=assignment.keys())
frontier = [r for r in dfs_variables
if r.owner is None or r in assignment.keys()]
cloned_inputs, cloned_outputs = clone_keep_replacements(frontier, [lik],
replacements=assignment)
cloned_lik, = cloned_outputs
return cloned_lik
