def _sample_control_dependencies(self, x):
"""Check counts for proper shape and values, then return tensor version."""
assertions = []
if not self.validate_args:
return assertions
assertions.extend(distribution_util.assert_nonnegative_integer_form(x))
return assertions
def _sample_control_dependencies(self, x):
assertions = []
if not self.validate_args:
return assertions
assertions.extend(distribution_util.assert_nonnegative_integer_form(x))
assertions.append(assert_util.assert_equal(
tf.ones([], dtype=x.dtype), tf.reduce_sum(x, axis=[-1]),
message='Last dimension of sample must sum to 1.'))
return assertions
def _sample_control_dependencies(self, x):
assertions = []
if not self.validate_args:
return assertions
assertions.extend(distribution_util.assert_nonnegative_integer_form(x))
assertions.append(
assert_util.assert_less_equal(x, tf.ones([], dtype=x.dtype),
message='Elements cannot exceed 1.'))
return assertions
def _parameter_control_dependencies(self, is_init):
assertions = categorical_lib.maybe_assert_categorical_param_correctness(
is_init, self.validate_args, self._probs, self._logits)
if not self.validate_args:
return assertions
if is_init != tensor_util.is_ref(self.total_count):
assertions.extend(distribution_util.assert_nonnegative_integer_form(
self.total_count))
return assertions
def _maybe_assert_valid_sample(self, counts):
"""Check counts for proper shape, values, then return tensor version."""
if not self.validate_args:
return []
assertions = distribution_util.assert_nonnegative_integer_form(counts)
assertions.append(assert_util.assert_equal(
self.total_count,
tf.reduce_sum(counts, axis=-1),
message='counts must sum to `self.total_count`'))
return assertions
def _sample_control_dependencies(self, x):
assertions = []
if not self.validate_args:
return assertions
assertions.extend(distribution_util.assert_nonnegative_integer_form(x))
assertions.append(
assert_util.assert_less_equal(
x, tf.cast(self._num_categories(), x.dtype),
message=('Categorical samples must be between `0` and `n-1` '
'where `n` is the number of categories.')))
return assertions
def _sample_control_dependencies(self, counts):
"""Check counts for proper shape, values, then return tensor version."""
assertions = []
if not self.validate_args:
return assertions
assertions.extend(distribution_util.assert_nonnegative_integer_form(counts))
assertions.append(assert_util.assert_equal(
self.total_count,
tf.reduce_sum(counts, axis=-1),
message='counts must sum to `self.total_count`'))
return assertions
def _sample_control_dependencies(self, x):
assertions = []
if not self.validate_args:
return assertions
assertions.extend(distribution_util.assert_nonnegative_integer_form(x))
assertions.append(
assert_util.assert_less_equal(
x, tf.cast(self._num_categories(), x.dtype),
message=('StoppingRatioLogistic samples must be `>= 0` and `<= K` '
'where `K` is the number of cutpoints.')))
return assertions
def _sample_control_dependencies(self, x):
"""Checks the validity of a sample."""
assertions = []
if not self.validate_args:
return assertions
assertions.extend(distribution_util.assert_nonnegative_integer_form(x))
assertions.append(assert_util.assert_equal(
self.total_count,
tf.reduce_sum(x, axis=-1),
message='counts last-dimension must sum to `self.total_count`'))
return assertions
def _sample_control_dependencies(self, counts):
"""Check counts for proper values."""
assertions = []
if not self.validate_args:
return assertions
assertions.extend(distribution_util.assert_nonnegative_integer_form(counts))
assertions.append(
assert_util.assert_less_equal(
counts, self.total_count,
message=('Sampled counts must be itemwise less than '
'or equal to `total_count` parameter.')))
return assertions
def _parameter_control_dependencies(self, is_init):
assertions = []
if is_init and self.validate_args:
# assert_categorical_event_shape handles both the static and dynamic case.
assertions.extend(
distribution_util.assert_categorical_event_shape(self._concentration))
if is_init != tensor_util.is_ref(self._total_count):
if self.validate_args:
assertions.extend(
distribution_util.assert_nonnegative_integer_form(
self._total_count))
if is_init != tensor_util.is_ref(self._concentration):
if self.validate_args:
assertions.append(
assert_util.assert_positive(
self._concentration,
message='Concentration parameter must be positive.'))
return assertions
def _sample_control_dependencies(self, x):
assertions = []
if not self.validate_args:
return assertions
assertions.extend(distribution_util.assert_nonnegative_integer_form(x))
return assertions
def _maybe_assert_valid_sample(self, x):
if not self.validate_args:
return []
return distribution_util.assert_nonnegative_integer_form(x)
