code: def prune(self, model):
          
  
          assert isinstance(model, ClassifierSet)
          assert model.algorithm is self
  
          # Determine the (virtual) population size.
          total_numerosity = sum(rule.numerosity for rule in model)
  
          # If the population size is already small enough, just return early
          if total_numerosity <= self.max_population_size:
              return []  # No rule's numerosity dropped to zero.
  
          # Determine the average fitness of the rules in the population.
          total_fitness = sum(rule.fitness for rule in model)
          average_fitness = total_fitness / total_numerosity
  
          # Determine the probability of deletion, as a function of both
          # accuracy and niche sparsity.
          total_votes = 0
          deletion_votes = {}
          for rule in model:
              vote = rule.action_set_size * rule.numerosity
  
              sufficient_experience = (
                  rule.experience > self.deletion_threshold
              )
              low_fitness = (
                  rule.fitness / rule.numerosity <
                  self.fitness_threshold * average_fitness
              )
              if sufficient_experience and low_fitness:
                  vote *= average_fitness / (rule.fitness /
                                             rule.numerosity)
  
              deletion_votes[rule] = vote
              total_votes += vote
  
          # Choose a rule to delete based on the probabilities just computed.
          selector = random.uniform(0, total_votes)
          for rule, vote in deletion_votes.items():
              selector -= vote
              if selector <= 0:
                  assert rule in model
                  if model.discard(rule):
                      return [rule]
                  else:
                      return []
  
          assert False
docs: Reduce the classifier set's population size, if necessary, by
          removing lower-quality *rules. Return a list containing any rules
          whose numerosities dropped to zero as a result of this call. (The
          list may be empty, if no rule's numerosity dropped to 0.) The
          model argument is a ClassifierSet instance which utilizes this
          algorithm.
  
          Usage:
              deleted_rules = model.algorithm.prune(model)
  
          Arguments:
              model: A ClassifierSet instance whose population may need to
                  be reduced in size.
          Return:
              A possibly empty list of ClassifierRule instances which were
              removed entirely from the classifier set because their
              numerosities dropped to 0.