tensorflow_probability/python/distributions/hidden_markov_model.py
def _marginal_hidden_probs(self):
    """Compute marginal pdf for each individual observable."""

    initial_log_probs = tf.broadcast_to(self._log_init,
                                        tf.concat([self.batch_shape_tensor(),
                                                   [self._num_states]],
                                                  axis=0))
    # initial_log_probs :: batch_shape num_states

    if self._num_steps > 1:
      transition_log_probs = self._log_trans

      def forward_step(log_probs, _):
        return _log_vector_matrix(log_probs, transition_log_probs)

      dummy_index = tf.zeros(self._num_steps - 1, dtype=tf.float32)

      forward_log_probs = tf.scan(forward_step, dummy_index,
                                  initializer=initial_log_probs,
                                  name="forward_log_probs")

      forward_log_probs = tf.concat([[initial_log_probs], forward_log_probs],
                                    axis=0)
    else:
      forward_log_probs = initial_log_probs[tf.newaxis, ...]

    # returns :: num_steps batch_shape num_states

    return tf.exp(forward_log_probs)
def _marginal_hidden_probs(self):
    """Compute marginal pdf for each individual observable."""

    initial_log_probs = tf.broadcast_to(self._log_init,
                                        tf.concat([self.batch_shape_tensor(),
                                                   [self._num_states]],
                                                  axis=0))
    # initial_log_probs :: batch_shape num_states

    if self._num_steps > 1:
      transition_log_probs = self._log_trans

      def forward_step(log_probs, _):
        return _log_vector_matrix(log_probs, transition_log_probs)

      dummy_index = tf.zeros(self._num_steps - 1, dtype=tf.float32)

      forward_log_probs = tf.scan(forward_step, dummy_index,
                                  initializer=initial_log_probs,
                                  name="forward_log_probs")

      forward_log_probs = tf.concat([[initial_log_probs], forward_log_probs],
                                    axis=0)
    else:
      forward_log_probs = initial_log_probs[tf.newaxis, ...]

    # returns :: num_steps batch_shape num_states

    return tf.exp(forward_log_probs)
Compute marginal pdf for each individual observable.