code: def all_points_core_distance(distance_matrix, d=2.0):
      
      distance_matrix[distance_matrix != 0] = (1.0 / distance_matrix[
          distance_matrix != 0]) ** d
      result = distance_matrix.sum(axis=1)
      result /= distance_matrix.shape[0] - 1
      result **= (-1.0 / d)
  
      return result
docs: Compute the all-points-core-distance for all the points of a cluster.
  
      Parameters
      ----------
      distance_matrix : array (cluster_size, cluster_size)
          The pairwise distance matrix between points in the cluster.
  
      d : integer
          The dimension of the data set, which is used in the computation
          of the all-point-core-distance as per the paper.
  
      Returns
      -------
      core_distances : array (cluster_size,)
          The all-points-core-distance of each point in the cluster
  
      References
      ----------
      Moulavi, D., Jaskowiak, P.A., Campello, R.J., Zimek, A. and Sander, J.,
      2014. Density-Based Clustering Validation. In SDM (pp. 839-847).