(self, melody, backing_chords)
return self._evaluate_log_likelihood([melody_copy],
                                         control_events=chords_copy)[0]
Evaluate the log likelihood of a melody conditioned on backing chords.

    Args:
      melody: The Melody object for which to evaluate the log likelihood.
      backing_chords: The backing chords, a ChordProgression object.

    Returns:
      The log likelihood of `melody` conditioned on `backing_chords` under this
      model.
Evaluate the log likelihood of a melody conditioned on backing chords.
Evaluate the log likelihood of a melody conditioned on backing chords .
def melody_log_likelihood(self, melody, backing_chords):
    """Evaluate the log likelihood of a melody conditioned on backing chords.

    Args:
      melody: The Melody object for which to evaluate the log likelihood.
      backing_chords: The backing chords, a ChordProgression object.

    Returns:
      The log likelihood of `melody` conditioned on `backing_chords` under this
      model.
    """
    melody_copy = copy.deepcopy(melody)
    chords_copy = copy.deepcopy(backing_chords)

    transpose_amount = melody_copy.squash(
        self._config.min_note,
        self._config.max_note,
        self._config.transpose_to_key)
    chords_copy.transpose(transpose_amount)

    return self._evaluate_log_likelihood([melody_copy],
                                         control_events=chords_copy)[0]
def melody_log_likelihood ( self , melody , backing_chords ) : melody_copy = copy . deepcopy ( melody ) chords_copy = copy . deepcopy ( backing_chords ) transpose_amount = melody_copy . squash ( self . _config . min_note , self . _config . max_note , self . _config . transpose_to_key ) chords_copy . transpose ( transpose_amount ) return self . _evaluate_log_likelihood ( [ melody_copy ] , control_events = chords_copy ) [ 0 ]