(self, num_compositions=10000, key=None,
                                    tonic_note=rl_tuner_ops.C_MAJOR_TONIC)
return stat_dict
Computes statistics about music theory rule adherence.

    Args:
      num_compositions: How many compositions should be randomly generated
        for computing the statistics.
      key: The numeric values of notes belonging to this key. Defaults to C
        Major if not provided.
      tonic_note: The tonic/1st note of the desired key.

    Returns:
      A dictionary containing the statistics.
Computes statistics about music theory rule adherence.
Computes statistics about music theory rule adherence .
def evaluate_music_theory_metrics(self, num_compositions=10000, key=None,
                                    tonic_note=rl_tuner_ops.C_MAJOR_TONIC):
    """Computes statistics about music theory rule adherence.

    Args:
      num_compositions: How many compositions should be randomly generated
        for computing the statistics.
      key: The numeric values of notes belonging to this key. Defaults to C
        Major if not provided.
      tonic_note: The tonic/1st note of the desired key.

    Returns:
      A dictionary containing the statistics.
    """
    stat_dict = rl_tuner_eval_metrics.compute_composition_stats(
        self,
        num_compositions=num_compositions,
        composition_length=self.num_notes_in_melody,
        key=key,
        tonic_note=tonic_note)

    return stat_dict
def evaluate_music_theory_metrics ( self , num_compositions = 10000 , key = None , tonic_note = rl_tuner_ops . C_MAJOR_TONIC ) : stat_dict = rl_tuner_eval_metrics . compute_composition_stats ( self , num_compositions = num_compositions , composition_length = self . num_notes_in_melody , key = key , tonic_note = tonic_note ) return stat_dict