def swap_flights(self,solution,f1,f2): old_gf1 = solution[f1] solution[f1] = solution[f2] solution[f2] = old_gf1 neighbor_ins = nb.Neighbor(solution, f1, old_gf1, solution[f1]) neighbor_ins.add_swap(f2, solution[f1], old_gf1) return neighbor_ins
def generate_neighborhood_one(self): for f in range(self.num_flights): for g in range(self.num_gates): if (self.current_solution[f] is not g) and (self.is_gate_compatible(g,f)) and (not self.tabu_list.is_tabu(f,self.current_solution[f],g,self.iteration)): neighbor_dict = self.current_solution.copy() neighbor_dict[f] = g # une fois une solution voisine est générée, on stocke le mouvement qui l'a générée neighbor_ins = nb.Neighbor(neighbor_dict, f, self.current_solution[f], g) self.neighborhood.append(neighbor_ins)
def action_process_lie(self, event_data):
(protocol_packet, (from_address, from_port)) = event_data
# TODO: This is a simplistic way of implementing the hold timer. Use a real timer instead.
self._time_ticks_since_lie_received = 0
# Sections B.1.4.1 and B.1.4.2
new_neighbor = neighbor.Neighbor(protocol_packet, from_address, from_port)
(accept, rule, offer_to_ztp, warning) = self.is_received_lie_acceptable(protocol_packet)
if not accept:
self._lie_accept_or_reject = "Rejected"
self._lie_accept_or_reject_rule = rule
if warning:
self.rx_warning("Received LIE packet rejected: %s", rule)
else:
self.rx_info("Received LIE packet rejected: %s", rule)
self.action_cleanup()
if offer_to_ztp:
self.send_offer_to_ztp_fsm(new_neighbor)
self.fsm.push_event(self.Event.UNACCEPTABLE_HEADER)
return
self._lie_accept_or_reject = "Accepted"
self._lie_accept_or_reject_rule = rule
# Section B.1.4.3
# Note: We send an offer to the ZTP state machine directly from here instead of pushing an
# UPDATE_ZTP_OFFER event (see deviation DEV-2 in doc/deviations)
self.send_offer_to_ztp_fsm(new_neighbor)
if not self.neighbor:
self.info("New neighbor detected with system-id %s",
utils.system_id_str(protocol_packet.header.sender))
self.neighbor = new_neighbor
self.fsm.push_event(self.Event.NEW_NEIGHBOR)
self.check_three_way()
return
# Section B.1.4.3.1
if new_neighbor.system_id != self.neighbor.system_id:
self.info("Neighbor system-id changed from %s to %s",
utils.system_id_str(self.neighbor.system_id),
utils.system_id_str(new_neighbor.system_id))
self.fsm.push_event(self.Event.MULTIPLE_NEIGHBORS)
return
# Section B.1.4.3.2
if new_neighbor.level != self.neighbor.level:
self.info("Neighbor level changed from %s to %s", self.neighbor.level,
new_neighbor.level)
self.fsm.push_event(self.Event.NEIGHBOR_CHANGED_LEVEL)
return
# Section B.1.4.3.3
if new_neighbor.address != self.neighbor.address:
self.info("Neighbor address changed from %s to %s", self.neighbor.address,
new_neighbor.address)
self.fsm.push_event(self.Event.NEIGHBOR_CHANGED_ADDRESS)
return
# Section B.1.4.3.4
if self.check_minor_change(new_neighbor):
self.fsm.push_event(self.Event.NEIGHBOR_CHANGED_MINOR_FIELDS)
self.neighbor = new_neighbor # TODO: The draft does not specify this, but it is needed
# Section B.1.4.3.5
self.check_three_way()
def train(self):
"""
映画のレコメンドモデル(最近傍法)の学習を行う
"""
if not os.path.exists(self.pickle_path): #データセットpickleがない場合、作成する
review_list = self.load_data(self.review_path, self.review_att, " ")
user_list = self.load_data(self.user_path, self.user_att, "\|")
movie_list = self.load_data(self.movie_path, self.movie_att, "\|")
data_list = self.merge_data(review_list, user_list, movie_list)
x_data, y_data, user_list = self.make_movie_vec(data_list)
with open(self.pickle_path, "wb") as f:
pickle.dump([x_data, y_data, user_list, movie_list], f)
else:
x_data, y_data, user_list, movie_list = self.load_dataset()
#テスト用にデータセットを100分割して、内1つをテストセットとする
n_fold = 100
k_fold = KFold(n_fold, shuffle=True)
for train_idx, test_idx in k_fold.split(x_data, y_data):
train_x = x_data[train_idx]
test_x = x_data[test_idx]
train_y = y_data[train_idx]
test_y = y_data[test_idx]
#最近傍法によるレコメンドシステムのインスタンスをつくる
nn = neighbor.Neighbor(train_x, train_y)
predict_test = nn.predict(test_x)
#映画IDから映画のタイトルを表示
predict_movie = []
for movie_id in predict_test:
predict_movie.append(self.id_to_title(movie_id, movie_list))
correct_movie = []
for movie_id in test_y:
correct_movie.append(self.id_to_title(movie_id, movie_list))
print("correct_label")
print(correct_movie)
print("predict_label")
print(predict_movie)
break
def generate_neighborhood_one(self):
"""
邻域动作1：考虑停机位与航班的兼容性，为每一个航班分配一个新的停机位。并只记录下不禁忌的邻域解。邻域解全部存储在内部变量 “self.neighborhood” 中。
两层for循环作用：采用2层循环以遍历所有航班与停机位。
if语句作用：1. 在循环遍历到的航班 f 中，循环遍历到的 g 是否与 f 当前搭配的停机位不同（相同就没必要换了）；
2. 判断此时循环遍历到的 g 是否与此时循环遍历到的 f 在机型上匹配（兼容性）；
3. 判断遍历到的 f 和 g 所组成的 “某航班 f 的停机位交换方案” 是否还在禁忌中；
只有：是、是、否，方可将此时遍历到的停机位 g 对此时遍历到的航班 f 的原本的停机位进行更改 """
for f in range(self.num_flights):
# for g in range(self.num_gates):
# for g in [random.randint(0, self.num_gates) for _ in range(int(self.num_gates / 2))]:
for g in random.sample([_ for _ in range(self.num_gates)],
int(self.num_gates / 2)):
if (self.current_solution[f] is not g) \
and (self.is_gate_compatible(g, f)) \
and (not self.tabu_list.is_tabu(f, self.current_solution[f], g,
self.iteration)): # 此句传入了：遍历到的航班f、f此时对应的停机位号、遍历到的停机位g、当前迭代次数
neighbor_dict = self.current_solution.copy(
) # 没必要使用deepcopy，copy()够用
neighbor_dict[f] = g # 将进入判断语句的 g 写入遍历到的 f 的对应停机位
# 一旦产生相邻的解决方案，就存储产生该解决方案的动作
neighbor_ins = nb.Neighbor(neighbor_dict, f,
self.current_solution[f], g)
self.neighborhood.append(neighbor_ins)