code: def cosine(brands, exemplars, weighted_avg=False, sqrt=False):
      
      scores = {}
      for brand, followers in brands:
          if weighted_avg:
              scores[brand] = np.average([_cosine(followers, others) for others in exemplars.values()],
                                         weights=[1. / len(others) for others in exemplars.values()])
          else:
              scores[brand] = 1. * sum(_cosine(followers, others) for others in exemplars.values()) / len(exemplars)
      if sqrt:
          scores = dict([(b, math.sqrt(s)) for b, s in scores.items()])
      return scores
docs: Return the cosine similarity betwee a brand's followers and the exemplars.