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):
    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 ) : 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.
Compute marginal pdf for each individual observable .