tensorflow_probability/python/distributions/gaussian_process_regression_model.py
def _is_empty_observation_data(
    feature_ndims, observation_index_points, observations):
  """Returns `True` if given observation data is empty.

  Emptiness means either
    1. Both `observation_index_points` and `observations` are `None`, or
    2. the "number of observations" shape is 0. The shape of
    `observation_index_points` is `[..., N, f1, ..., fF]`, where `N` is the
    number of observations and the `f`s are feature dims. Thus, we look at the
    shape element just to the left of the leftmost feature dim. If that shape is
    zero, we consider the data empty.

  We don't check the shape of observations; validations are checked elsewhere in
  the calling code, to ensure these shapes are consistent.

  Args:
    feature_ndims: the number of feature dims, as reported by the GP kernel.
    observation_index_points: the observation data locations in the index set.
    observations: the observation data.

  Returns:
    is_empty: True if the data were deemed to be empty.
  """
  # If both input locations and observations are `None`, we consider this
  # "empty" observation data.
  if observation_index_points is None and observations is None:
    return True
  num_obs = tf.compat.dimension_value(
      observation_index_points.shape[-(feature_ndims + 1)])
  if num_obs is not None and num_obs == 0:
    return True
  return False
def _is_empty_observation_data(
    feature_ndims, observation_index_points, observations):
  # If both input locations and observations are `None`, we consider this
  # "empty" observation data.
  if observation_index_points is None and observations is None:
    return True
  num_obs = tf.compat.dimension_value(
      observation_index_points.shape[-(feature_ndims + 1)])
  if num_obs is not None and num_obs == 0:
    return True
  return False
def _is_empty_observation_data ( feature_ndims , observation_index_points , observations ) : # If both input locations and observations are `None`, we consider this # "empty" observation data. if observation_index_points is None and observations is None : return True num_obs = tf . compat . dimension_value ( observation_index_points . shape [ - ( feature_ndims + 1 ) ] ) if num_obs is not None and num_obs == 0 : return True return False
Returns `True` if given observation data is empty.

  Emptiness means either
    1. Both `observation_index_points` and `observations` are `None`, or
    2. the "number of observations" shape is 0. The shape of
    `observation_index_points` is `[..., N, f1, ..., fF]`, where `N` is the
    number of observations and the `f`s are feature dims. Thus, we look at the
    shape element just to the left of the leftmost feature dim. If that shape is
    zero, we consider the data empty.

  We don't check the shape of observations; validations are checked elsewhere in
  the calling code, to ensure these shapes are consistent.

  Args:
    feature_ndims: the number of feature dims, as reported by the GP kernel.
    observation_index_points: the observation data locations in the index set.
    observations: the observation data.

  Returns:
    is_empty: True if the data were deemed to be empty.
Returns True if given observation data is empty .