def result(self, state, action, player):
newState = []
T = [state[0] + 1]
A, A1, A2, IA = list(state[1]), list(state[2]), list(state[3]), list(state[4])
A.append(action[1])
IA.remove(action[1])
if player == self.PLAYERS[0]:
A1.append(action[1])
newState = Spreading(A, A1, A2, IA)
else:
A2.append(action[1])
newState = Spreading(A, A1, A2, IA)
newState = T + newState
return newState
def breadth_first_search(R, T, buf, M):
global args
K = args.seed_size
TURNS = K + 1
SEED = []
fileName = "Path.csv"
output = open(fileName, "w")
cnt = R
SEED = []
for x in range(7):
SEED.append(buf[x][1])
output.write("{0},{1},{2}\n".format(x + 1, buf[x][1], buf[x][0]))
Active = []
Active_1 = []
Active_2 = []
TREND = []
Inactive = list(setting.data.threshold.keys())
for i in range(K):
Active.append(SEED[i])
Inactive.remove(SEED[i])
if i % 2 == 0:
Active_1.append(SEED[i])
else:
Active_2.append(SEED[i])
(Active, Active_1, Active_2,
Inactive) = Spreading(Active, Active_1, Active_2, Inactive)
if i % 2 == 1:
TREND.append(len(Active) / len(setting.data.threshold))
output.write("{0}\n{1}\n{2}\n".format(len(Active), len(Active_1),
len(Active_2)))
output.write("MCT Construction time: {0}\n".format(T))
for t in TREND:
output.write("{0},".format("{:.2%}".format(t)))
output.write("\n\n")
output.close()
METHODS = ['MaxWeight', 'MajorityWeight', 'MaxPageRank', 'MaxLDAG']
INDEX = METHODS.index(M.__name__)
output = open("Result.csv", "w")
for i in range(4):
STR = 'AI'
output.write(M.__name__ + ' VS ' + METHODS[i] + '\n')
outcome = AIVSSTUPID(M.__name__, METHODS[i], None)
output.write('p1:{0},p2:{1}\n'.format(outcome[0], outcome[1]))
output.write(METHODS[i] + ' VS ' + M.__name__ + '\n')
outcome = AIVSSTUPID(METHODS[i], M.__name__, None)
output.write('p1:{0},p2:{1}\n'.format(outcome[0], outcome[1]))
output.write('AI VS ' + METHODS[i] + '\n')
outcome1 = AIVSSTUPID(STR, METHODS[i], fileName)
output.write('p1:{0},p2:{1}\n\n'.format(outcome1[0], outcome1[1]))
output.write(METHODS[i] + ' VS ' + 'AI' + '\n')
outcome2 = AIVSSTUPID(METHODS[i], STR, fileName)
output.write('p1:{0},p2:{1}\n\n'.format(outcome2[0], outcome2[1]))
output.close()
def resultofallmethods():
Result = []
METHODS = [MaxWeight, MajorityWeight, MaxPageRank, MaxLDAG]
for M in METHODS:
Active, Active1, Active2 = [], [], []
Inactive = [x for x in setting.data.threshold.keys()]
seed = []
for i in range(10):
X = M(list(Active), list(Active_1), list(Active_2), list(Inactive))
Active.append(X)
Active_1.append(X)
Inactive.remove(X)
(Active, Active_1, Active_2,
Inactive) = Spreading(Active, Active_1, Active_2, Inactive)
Result.append(len(Active_1))
return Result
def simulation(self, METHOD):
Actions = METHOD
reward = 0.0
for A in Actions:
tmp_turn = self.state[0]
st = [self.state[0], list(self.state[1]), list(self.state[2]), list(self.state[3]), list(self.state[4])]
p1 = self.player
before,before1 = 0,0
if p1=='2':
before = len(self.parent.state[2])
before1 = len(self.parent.state[3])
else:
before = len(self.parent.state[3])
before1 = len(self.parent.state[2])
steps = 10
while not self.ccim.terminal(tmp_turn):
if p1 == '1':
#print("player1 selects--------------------")
if self.player=='1': action = A(list(st[1]),list(st[2]),list(st[3]),list(st[4]))
else:
action = A(list(st[1]),list(st[2]),list(st[3]),list(st[4]))
st[1].append(action)
st[2].append(action)
else:
#print("player2 selects-------------------")
if self.player=='2': action = A(list(st[1]),list(st[3]),list(st[2]),list(st[4]))
else:
action = A(list(st[1]),list(st[3]),list(st[2]),list(st[4]))
st[1].append(action)
st[3].append(action)
tmp = Spreading(st[1], st[2], st[3], st[4])
tmp_turn += 1
st = [tmp_turn]
st.extend(tmp)
steps -= 1
if steps==0: break
p1 = self.ccim.next_player(p1)
Outcome = 0
now_round = 0
Outcome = self.ccim.outcome(st,p1)
Outcome1 = self.ccim.outcome(st,self.ccim.next_player(p1))
now_round = tmp_turn - 1
reward += ((Outcome-before))
reward /= len(Actions)
return reward
def gen_random(active, my_active, your_active, inactive, left_turns):
gen_seed = []
for x in range(left_turns*2):
seed = choice(inactive)
print("Random seed: %s"%(seed))
gen_seed.append(seed)
active.append(seed)
inactive.remove(seed)
if x%2 == 0:
my_active.append(seed)
else:
your_active.append(seed)
cnt = Spreading(list(active), list(my_active), list(your_active), list(inactive))
active = list(cnt[0])
my_active = list(cnt[1])
your_active = list(cnt[2])
inactive = list(cnt[3])
return Chromosome(gen_seed, Chromosome._update_fitness(my_active, your_active), list(active), list(my_active), list(your_active), list(inactive))
def _update_active_state(gene, active, my_active, your_active, inactive): tmp_active = list(active) tmp_my_active = list(my_active) tmp_your_active = list(your_active) tmp_inactive = list(inactive) for i in range(len(gene)): if gene[i] in tmp_active: continue tmp_active.append(gene[i]) tmp_inactive.remove(gene[i]) if i%2==0: tmp_my_active.append(gene[i]) else: tmp_your_active.append(gene[i]) cnt = Spreading(list(tmp_active), list(tmp_my_active), list(tmp_your_active), list(tmp_inactive)) tmp_active = list(cnt[0]) tmp_my_active = list(cnt[1]) tmp_your_active = list(cnt[2]) tmp_inactive = list(cnt[3]) return [Chromosome._update_fitness(my_active, your_active), tmp_active, tmp_my_active, tmp_your_active, tmp_inactive]
if x%2==0: Method.append(AI[x])
else: Method.append(p2)
elif p2=="AI":
for x in range(20):
if x%2==1: Method.append(AI[x])
else: Method.append(p1)
else:
for x in range(20):
if x%2==0: Method.append(p1)
else: Method.append(p2)
Active, Active_1, Active_2 = [], [], []
Inactive = [x for x in setting.data.threshold.keys()]
seed = '1'
for x in range(20):
if x%2==0:
seed = DIC[Method[x]](list(Active),list(Active_1),list(Active_2),list(Inactive))
if not seed:
seed = MaxWeight(list(Active),list(Active_1),list(Active_2),list(Inactive))
Active.append(seed)
Active_1.append(seed)
Inactive.remove(seed)
else:
seed = DIC[Method[x]](list(Active),list(Active_2),list(Active_1),list(Inactive))
if not seed:
seed = MaxWeight(list(Active),list(Active_2),list(Active_1),list(Inactive))
Active.append(seed)
Active_2.append(seed)
Inactive.remove(seed)
(Active, Active_1, Active_2, Inactive) = Spreading(list(Active),list(Active_1),list(Active_2),list(Inactive))
return (len(Active_1),len(Active_2))