def log_likelihood(self, RVs, observations, llik):
"""
The output from this function may be a random variable, if not all sources
of randomness are observed.
llik - a vector to which observation log-likelihoods will be added.
"""
if len(RVs) != len(observations):
raise TypeError("RVs and observations must have same length")
for iv in RVs:
if not montetheano.rv.is_rv(iv.vals):
raise ValueError("non-random var in RVs element", iv)
assignment = {}
idxs_of = {}
for rv, o in zip(RVs, observations):
assignment[rv.vals] = o.vals
idxs_of[o.vals] = o.idxs
# 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
# raw_RVs = [v for v in ancestors(RVs.flatten())
# if montetheano.rv.is_raw_rv(v)]
# Cast assignment elements to the right kind of thing
assignment = montetheano.rv.typed_items(assignment)
for rv_vals, obs_vals in assignment.items():
lpdf = montetheano.rv.lpdf(rv_vals, obs_vals)
llik = tensor.inc_subtensor(llik[idxs_of[obs_vals]], lpdf)
# rewire the graph so that the posteriors depend on other
# observations instead of each other.
involved = [llik] + RVs.flatten() + observations.flatten()
inputs = theano.gof.graph.inputs(involved)
env = ienv.std_interactive_env(inputs, involved, clone_inputs_and_orphans=False)
env.replace_all_sorted(
zip(RVs.flatten(), observations.flatten()), reason="IndependentNodeTreeEstimator.log_likelihood"
)
# raise an exception if we created cycles
env.toposort()
rval = env.newest(llik)
env.disown()
return rval
def posterior(self, priors, observations, s_rng):
"""
priors - an IdxsValsList of random variables
observations - an IdxsValsList of corresponding samples
returns - an IdxsValsList of posterior random variables
"""
assert len(priors) == len(observations)
# observation.idxs could be invalid.
# They could be invalid, in the sense of describing samples that
# could not have been drawn from the prior.
# This code does not try to detect that situation.
post_vals = [self.s_posterior_helper(p, o, s_rng) for p, o in zip(priors, observations)]
# At this point, each post_vals[i] is connected to the original graph
# formed of prior nodes.
#
# We want each post_vals[i] to be connected instead to the other
# post_vals just created, corresponding to the posterior values of other
# random vars.
#
# The way to get all the new_s_val nodes hooked up to one another is to
# use the clone_keep_replacements function.
# XXX: this clones everything. It should be possible to do a more
# selective clone of just the pieces that change.
inputs = theano.gof.graph.inputs(priors.flatten() + post_vals)
env = ienv.std_interactive_env(inputs, priors.flatten() + post_vals, clone_inputs_and_orphans=False)
env.prefer_replace(zip(priors.valslist(), post_vals), reason="IndependentNodeTreeEstimator.posterior")
# raise an exception if we created cycles
env.toposort()
# extract the cloned results from the env
rval = IdxsValsList.fromlists([env.newest(v) for v in priors.idxslist()], [env.newest(v) for v in post_vals])
# remove all references in the variables to the env. Prepare them
# to be inserted into another env if necessary.
env.disown()
return rval
