code: def get_row_missing(xc, xd, cdiffs, index, cindices, dindices):
      """Calculate distance between index instance and all other instances."""
      row = np.empty(0, dtype=np.double)  # initialize empty row
      cinst1 = xc[
          index
      ]  # continuous-valued features for index instance
      dinst1 = xd[index]  # discrete-valued features for index instance
      # Boolean mask locating missing values for continuous features for index instance
      can = cindices[index]
      # Boolean mask locating missing values for discrete features for index instance
      dan = dindices[index]
      tf = len(cinst1) + len(dinst1)  # total number of features.
  
      # Progressively compare current instance to all others. Excludes comparison with self indexed instance. (Building the distance matrix triangle).
      for j in range(index):
          dist = 0
          dinst2 = xd[
              j
          ]  # discrete-valued features for compared instance
          cinst2 = xc[
              j
          ]  # continuous-valued features for compared instance
  
          # Manage missing values in discrete features
          # Boolean mask locating missing values for discrete features for compared instance
          dbn = dindices[j]
          # indexes where there is at least one missing value in the feature between an instance pair.
          idx = np.unique(np.append(dan, dbn))
          # Number of features excluded from distance calculation due to one or two missing values within instance pair. Used to normalize distance values for comparison.
          dmc = len(idx)
          d1 = np.delete(
              dinst1, idx
          )  # delete unique missing features from index instance
          d2 = np.delete(
              dinst2, idx
          )  # delete unique missing features from compared instance
  
          # Manage missing values in continuous features
          # Boolean mask locating missing values for continuous features for compared instance
          cbn = cindices[j]
          # indexes where there is at least one missing value in the feature between an instance pair.
          idx = np.unique(np.append(can, cbn))
          # Number of features excluded from distance calculation due to one or two missing values within instance pair. Used to normalize distance values for comparison.
          cmc = len(idx)
          c1 = np.delete(
              cinst1, idx
          )  # delete unique missing features from index instance
          c2 = np.delete(
              cinst2, idx
          )  # delete unique missing features from compared instance
          # delete unique missing features from continuous value difference scores
          cdf = np.delete(cdiffs, idx)
  
          # Add discrete feature distance contributions (missing values excluded) - Hamming distance
          dist += len(d1[d1 != d2])
  
          # Add continuous feature distance contributions (missing values excluded) - Manhattan distance (Note that 0-1 continuous value normalization is included ~ subtraction of minimums cancel out)
          dist += np.sum(np.absolute(np.subtract(c1, c2)) / cdf)
  
          # Normalize distance calculation based on total number of missing values bypassed in either discrete or continuous features.
          tnmc = (
              tf - dmc - cmc
          )  # Total number of unique missing counted
          # Distance normalized by number of features included in distance sum (this seeks to handle missing values neutrally in distance calculation)
          dist = dist / float(tnmc)
  
          row = np.append(row, dist)
  
      return row
  
file_path: scikit-rebate/skrebate/scoring_utils.py
imports: import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: get_row_missing(xc, xd, cdiffs, index, cindices, dindices) and returns: row