X : array-like, shape (n_samples, n_features)
    List of n_features-dimensional data points. Each row corresponds
    to a single data point.
labels : array-like, shape (n_samples,)
    Predicted labels for each sample.  (-1 - for noise)
metric : str,
    The distance metric, can be ‘braycurtis’, ‘canberra’, ‘chebyshev’, ‘cityblock’, ‘correlation’,
    ‘cosine’, ‘dice’, ‘euclidean’, ‘hamming’, ‘jaccard’, ‘kulsinski’, ‘mahalanobis’, ‘matching’, ‘minkowski’,
    ‘rogerstanimoto’, ‘russellrao’, ‘seuclidean’, ‘sokalmichener’, ‘sokalsneath’, ‘sqeuclidean’, ‘wminkowski’,
    ‘yule’.
alg_noise : str,
    Algorithm for recording noise points.
    'comb' - combining all noise points into one cluster (default)
    'sep' - definition of each noise point as a separate cluster
    'bind' -  binding of each noise point to the cluster nearest from it
    'filter' - filtering noise points
intra_dens_inf : bool,
    If False (default) CDbw index = 0 for cohesion or compactness - inf or nan.
s : int,
    Number of art representative points. (>2)
multipliers : bool,
    Format of output. False (default) - only CDbw index, True - tuple (compactness, cohesion, separation, CDbw)

cdbw : float,
    The resulting CDbw validity index.