evaluate_new.py 文件源码

def _select_best_measure_index(curr_measures, args):
    idx = None
    try:
        if args.measure == 'aicc':
            # The best score for AICc is the minimum.
            idx = np.nanargmin(curr_measures)
        elif args.measure in ['hmm-distance', 'wasserstein', 'mahalanobis']:
            # The best score for the l-d measure is the maximum.
            idx = np.nanargmax(curr_measures)
    except:
        idx = random.choice(range(len(curr_measures)))
    assert idx is not None
    return idx