code: def adamic(brands, exemplars):
      
      print('adamic deprecated...requires loading all brands in memory.')
      return
      degrees = compute_log_degrees(brands, exemplars)
      scores = {}
      exemplar_sums = dict([(exemplar, sum(degrees[z] for z in exemplars[exemplar])) for exemplar in exemplars])
  
      for brand in sorted(brands):
          brand_sum = sum(degrees[z] for z in brands[brand])
          total = 0.
          for exemplar in exemplars:
              total += sum(degrees[z] for z in brands[brand] & exemplars[exemplar]) / (brand_sum + exemplar_sums[exemplar])
          scores[brand] = total / len(exemplars)
      return scores
docs: Return the average Adamic/Adar similarity between a brand's followers
      and the followers of each exemplar. We approximate the number of followed
      accounts per user by only considering those in our brand set.