def confidences(cluster, condensed_distances, d):
    '''
    We calculate a per record score that is similar to a standard
    deviation.  The main reason is that these record scores can be
    used to calculate the standard deviation of an entire cluster,
    which is a reasonable metric for clusters.
    '''

    scores = dict.fromkeys(cluster, 0.0)
    squared_distances = condensed_distances ** 2
    for i, j in itertools.combinations(cluster, 2):
        index = d * (d - 1) / 2 - (d - i) * (d - i - 1) / 2 + j - i - 1
        squared_dist = squared_distances[int(index)]
        scores[i] += squared_dist
        scores[j] += squared_dist
    scores = numpy.array([score for _, score in sorted(scores.items())])
    scores /= len(cluster) - 1
    scores = numpy.sqrt(scores)
    scores = 1 - scores
    return scores
We calculate a per record score that is similar to a standard
    deviation.  The main reason is that these record scores can be
    used to calculate the standard deviation of an entire cluster,
    which is a reasonable metric for clusters.