def _reduce_log_probs_over_dists(self, lps):
if self._experimental_use_kahan_sum:
return sum(jd_lib.maybe_check_wont_broadcast(
self._reduce_measure_over_dists(
lps, reduce_fn=tfp_math.reduce_kahan_sum),
self.validate_args)).total
else:
return sum(jd_lib.maybe_check_wont_broadcast(
self._reduce_measure_over_dists(lps, reduce_fn=tf.reduce_sum),
self.validate_args))
def _entropy(self):
"""Shannon entropy in nats."""
if any(self._dist_fn_args):
raise ValueError(
'Can only compute entropy when all distributions are independent.')
return sum(joint_distribution_lib.maybe_check_wont_broadcast(
(d().entropy() for d in self._dist_fn_wrapped),
self.validate_args))
def _entropy(self):
"""Shannon entropy in nats."""
if not all(self._is_distribution_instance):
raise ValueError(
'Can only compute entropy when all distributions are independent.'
)
return sum(
joint_distribution_lib.maybe_check_wont_broadcast(
(d.entropy() for d in self.distribution_fn),
self.validate_args))
def _cross_entropy(self, other):
if (not isinstance(other, JointDistributionSequential) or
len(self.model) != len(other.model)):
raise ValueError(
'Can only compute cross entropy between `JointDistributionSequential`s '
'with the same number of component distributions.')
if any(self._dist_fn_args) or any(other._dist_fn_args): # pylint: disable=protected-access
raise ValueError(
'Can only compute cross entropy when all component distributions '
'are independent.')
return sum(joint_distribution_lib.maybe_check_wont_broadcast(
(d0().cross_entropy(d1()) for d0, d1
in zip(self._dist_fn_wrapped, other._dist_fn_wrapped)), # pylint: disable=protected-access
self.validate_args))
