code: def ng_dissim(a, b, X=None, membship=None):
      """Ng et al.'s dissimilarity measure, as presented in
      Michael K. Ng, Mark Junjie Li, Joshua Zhexue Huang, and Zengyou He, "On the
      Impact of Dissimilarity Measure in k-Modes Clustering Algorithm", IEEE
      Transactions on Pattern Analysis and Machine Intelligence, Vol. 29, No. 3,
      January, 2007
  
      This function can potentially speed up training convergence.
  
      Note that membship must be a rectangular array such that the
      len(membship) = len(a) and len(membship[i]) = X.shape[1]
  
      In case of missing membship, this function reverts back to
      matching dissimilarity (e.g., when predicting).
      """
      # Without membership, revert to matching dissimilarity
      if membship is None:
          return matching_dissim(a, b)
  
      def calc_cjr(b, X, memj, idr):
          """Num objects w/ category value x_{i,r} for rth attr in jth cluster"""
          xcids = np.where(memj == 1)
          return float(
              (np.take(X, xcids, axis=0)[0][:, idr] == b[idr]).sum(0)
          )
  
      def calc_dissim(b, X, memj, idr):
          # Size of jth cluster
          cj = float(np.sum(memj))
          return (
              (1.0 - (calc_cjr(b, X, memj, idr) / cj))
              if cj != 0.0
              else 0.0
          )
  
      if len(membship) != a.shape[0] and len(membship[0]) != X.shape[1]:
          raise ValueError(
              "'membship' must be a rectangular array where "
              "the number of rows in 'membship' equals the "
              "number of rows in 'a' and the number of "
              "columns in 'membship' equals the number of rows in 'X'."
          )
  
      return np.array(
          [
              np.array(
                  [
                      calc_dissim(b, X, membship[idj], idr)
                      if b[idr] == t
                      else 1.0
                      for idr, t in enumerate(val_a)
                  ]
              ).sum(0)
              for idj, val_a in enumerate(a)
          ]
      )
  
imports: import numpy
label_desc: Use image node_id 5 for calling a global function with example usage: ng_dissim(a, b, X, membship) and returns: np, matching_dissim, float, unknown