def testRegistrationFailures(self):
class MyDist(normal.Normal):
pass
with self.assertRaisesRegexp(TypeError, "must be callable"):
kullback_leibler.RegisterKL(MyDist, MyDist)("blah")
# First registration is OK
kullback_leibler.RegisterKL(MyDist, MyDist)(lambda a, b: None)
# Second registration fails
with self.assertRaisesRegexp(ValueError,
"has already been registered"):
kullback_leibler.RegisterKL(MyDist, MyDist)(lambda a, b: None)
if not self.validate_args:
return []
assertions = []
if is_init != tensor_util.is_ref(self.concentration):
assertions.append(
assert_util.assert_positive(
self.concentration,
message='Argument `concentration` must be positive.'))
if self.rate is not None and is_init != tensor_util.is_ref(self.rate):
assertions.append(
assert_util.assert_positive(
self.rate, message='Argument `rate` must be positive.'))
return assertions
kullback_leibler.RegisterKL(ExpGamma, ExpGamma)(gamma_lib.kl_gamma_gamma)
# TODO(b/182603117): Remove `AutoCompositeTensor` subclass when
# `TransformedDistribution` is converted to `CompositeTensor`.
class ExpInverseGamma(transformed_distribution.TransformedDistribution,
distribution.AutoCompositeTensorDistribution):
"""ExpInverseGamma distribution.
The `ExpInverseGamma` distribution is defined over the real numbers such that
X ~ ExpInverseGamma(..) => exp(X) ~ InverseGamma(..).
The distribution is logically equivalent to `tfb.Log()(tfd.InverseGamma(..))`,
but can be sampled with much better precision.
#### Mathematical Details