tensorflow_probability/python/distributions/linear_gaussian_ssm.py
def build_kalman_sample_step(get_transition_matrix_for_timestep,
                             get_transition_noise_for_timestep,
                             get_observation_matrix_for_timestep,
                             get_observation_noise_for_timestep,
                             full_sample_and_batch_shape,
                             stream,
                             validate_args=False):
  """Build a callable for one step of Kalman sampling recursion.

  Args:
    get_transition_matrix_for_timestep: callable taking a timestep
      as an integer `Tensor` argument, and returning a `LinearOperator`
      of shape `[latent_size, latent_size]`.
    get_transition_noise_for_timestep: callable taking a timestep as
      an integer `Tensor` argument, and returning a
      `MultivariateNormalLinearOperator` of event shape
      `[latent_size]`.
    get_observation_matrix_for_timestep: callable taking a timestep
      as an integer `Tensor` argument, and returning a `LinearOperator`
      of shape `[observation_size, observation_size]`.
    get_observation_noise_for_timestep: callable taking a timestep as
      an integer `Tensor` argument, and returning a
      `MultivariateNormalLinearOperator` of event shape
      `[observation_size]`.
    full_sample_and_batch_shape: Desired sample and batch shape of the
      returned samples, concatenated in a single `Tensor`.
    stream: `tfd.SeedStream` instance used to generate a
      sequence of random seeds.
    validate_args: if True, perform error checking at runtime.

  Returns:
    sample_step: a callable that samples the latent state and
      observation at time `t`, given latent state at time `t-1`.
  """

  def sample_step(sampled_prev, t):
    """Sample values for a single timestep."""
    latent_prev, _ = sampled_prev

    transition_matrix = get_transition_matrix_for_timestep(t - 1)
    transition_noise = get_transition_noise_for_timestep(t - 1)

    latent_pred = transition_matrix.matmul(latent_prev)
    latent_sampled = latent_pred + transition_noise.sample(
        sample_shape=_augment_sample_shape(
            transition_noise,
            full_sample_and_batch_shape,
            validate_args),
        seed=stream())[..., tf.newaxis]

    observation_matrix = get_observation_matrix_for_timestep(t)
    observation_noise = get_observation_noise_for_timestep(t)

    observation_pred = observation_matrix.matmul(latent_sampled)
    observation_sampled = observation_pred + observation_noise.sample(
        sample_shape=_augment_sample_shape(
            observation_noise,
            full_sample_and_batch_shape,
            validate_args),
        seed=stream())[..., tf.newaxis]

    return (latent_sampled, observation_sampled)

  return sample_step
def build_kalman_sample_step(get_transition_matrix_for_timestep,
                             get_transition_noise_for_timestep,
                             get_observation_matrix_for_timestep,
                             get_observation_noise_for_timestep,
                             full_sample_and_batch_shape,
                             stream,
                             validate_args=False):
  def sample_step(sampled_prev, t):
    """Sample values for a single timestep."""
    latent_prev, _ = sampled_prev

    transition_matrix = get_transition_matrix_for_timestep(t - 1)
    transition_noise = get_transition_noise_for_timestep(t - 1)

    latent_pred = transition_matrix.matmul(latent_prev)
    latent_sampled = latent_pred + transition_noise.sample(
        sample_shape=_augment_sample_shape(
            transition_noise,
            full_sample_and_batch_shape,
            validate_args),
        seed=stream())[..., tf.newaxis]

    observation_matrix = get_observation_matrix_for_timestep(t)
    observation_noise = get_observation_noise_for_timestep(t)

    observation_pred = observation_matrix.matmul(latent_sampled)
    observation_sampled = observation_pred + observation_noise.sample(
        sample_shape=_augment_sample_shape(
            observation_noise,
            full_sample_and_batch_shape,
            validate_args),
        seed=stream())[..., tf.newaxis]

    return (latent_sampled, observation_sampled)

  return sample_step
def build_kalman_sample_step ( get_transition_matrix_for_timestep , get_transition_noise_for_timestep , get_observation_matrix_for_timestep , get_observation_noise_for_timestep , full_sample_and_batch_shape , stream , validate_args = False ) : def sample_step ( sampled_prev , t ) : """Sample values for a single timestep.""" latent_prev , _ = sampled_prev transition_matrix = get_transition_matrix_for_timestep ( t - 1 ) transition_noise = get_transition_noise_for_timestep ( t - 1 ) latent_pred = transition_matrix . matmul ( latent_prev ) latent_sampled = latent_pred + transition_noise . sample ( sample_shape = _augment_sample_shape ( transition_noise , full_sample_and_batch_shape , validate_args ) , seed = stream ( ) ) [ ... , tf . newaxis ] observation_matrix = get_observation_matrix_for_timestep ( t ) observation_noise = get_observation_noise_for_timestep ( t ) observation_pred = observation_matrix . matmul ( latent_sampled ) observation_sampled = observation_pred + observation_noise . sample ( sample_shape = _augment_sample_shape ( observation_noise , full_sample_and_batch_shape , validate_args ) , seed = stream ( ) ) [ ... , tf . newaxis ] return ( latent_sampled , observation_sampled ) return sample_step
Build a callable for one step of Kalman sampling recursion.

  Args:
    get_transition_matrix_for_timestep: callable taking a timestep
      as an integer `Tensor` argument, and returning a `LinearOperator`
      of shape `[latent_size, latent_size]`.
    get_transition_noise_for_timestep: callable taking a timestep as
      an integer `Tensor` argument, and returning a
      `MultivariateNormalLinearOperator` of event shape
      `[latent_size]`.
    get_observation_matrix_for_timestep: callable taking a timestep
      as an integer `Tensor` argument, and returning a `LinearOperator`
      of shape `[observation_size, observation_size]`.
    get_observation_noise_for_timestep: callable taking a timestep as
      an integer `Tensor` argument, and returning a
      `MultivariateNormalLinearOperator` of event shape
      `[observation_size]`.
    full_sample_and_batch_shape: Desired sample and batch shape of the
      returned samples, concatenated in a single `Tensor`.
    stream: `tfd.SeedStream` instance used to generate a
      sequence of random seeds.
    validate_args: if True, perform error checking at runtime.

  Returns:
    sample_step: a callable that samples the latent state and
      observation at time `t`, given latent state at time `t-1`.
Build a callable for one step of Kalman sampling recursion .