code: 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 scoref 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
docs: 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.