def expression(): #随机生成表达式
symbol = ['+', '-', '*', '/']
brackets = ['(', '', ')']
# 随机产生计算符
s1 = randint(0, 2)
s2 = randint(0, 3)
s3 = randint(0, 3)
#随机产生括号bt表示左括号，br表示右括号
bt1 = randint(0, 1)
bt2 = randint(0, 1)
bt3 = randint(0, 1)
br1 = randint(1, 2)
br2 = randint(1, 2)
br3 = randint(1, 2)
if bt1 == 0:
bt2 = 1
bt3 = 1
if br1 == 2:
br2 = 1
br3 = 1
else:
br2 = 2
br3 = 1
else:
if bt2 == 0:
bt3 = 1
br1 = 1
if (br2 == 2):
br3 = 1
else:
br3 = 2
else:
bt3 = 0
br1 = 1
br2 = 1
br3 = 2
num1 = uf(0, 1)
# 对随机产生的分子分母做最大限制
num1 = fr(num1).limit_denominator(10)
num2 = uf(0, 1)
num2 = fr(num2).limit_denominator(10)
num3 = randint(1, 10)
num4 = randint(1, 10)
# 产生随机表达式
ran_exp = brackets[bt1] + str(num1) + symbol[s1] + brackets[bt2] + str(num2) + brackets[br1] + \
symbol[s2] + brackets[bt3] + str(num3) + brackets[br2] + symbol[s3] + str(num4) + brackets[br3]
ran_exp = str(ran_exp)
return ran_exp
def test(self, state_dict=None):
if state_dict is not None:
print('load')
self.policy_net.load_state(state_dict)
for _ in range(50):
self.rocket.set_state(uf(-100, 100), uf(650, 750), uf(-2.5, 2.5),
uf(-20, 20), uf(-120, -80), uf(-0.6, 0.6))
while not self.rocket.dead:
state_0 = torch.tensor(self.rocket.get_state(),
device=self.device).float()
# select next action
with torch.no_grad():
out = self.policy_net(state_0)
action_idx = torch.max(out, dim=0)[1].item()
# execute action (state_0, action, reward, state_1, terminal)
action = ACTION[action_idx] # to feed the rocket
transition = self.rocket.update(*action)
self.render.frame(self.rocket, transition[2], realtime=True)
def random_longitude()->str:
return '%.7f'% uf(-180,180)
def random_latitude()->str:
return '%.7f'% uf(-90,90)
def mkjson():
global now_time
global data
global time_mac
global start_id
global data_s
temp = {}
stime = datetime.datetime.strptime(data['time'],'%a %b %d %H:%M:%S %Y')
time = stime + datetime.timedelta(seconds = 60)
now_time = time
etime = time.strftime('%a %b %d %H:%M:%S %Y')
hour = time.hour
mac_count = 0
mac_incount = len(time_mac.keys())
mac_outcount = 0
mac_list = []
mac0 = '00:00:00:00'
mac1 = 0
mac2 = 0
mac_tmp = mac_incount
if hour >= 6 and hour <= 18:
mac_count = rg(20,30)
mac_tmp = rg(8,12)
elif hour > 18 and hour <=24:
mac_count = rg(18,28)
mac_tmp = rg(6,10)
elif hour >= 0 and hour < 6:
mac_count = rg(12,24)
mac_tmp = rg(4,8)
if len(time_mac.keys()) != 0:
for x in time_mac.keys():
if (time_mac[x] - time).total_seconds() <= 0:
time_mac.pop(x)
mac_incount-=1
else:
s = {}
s['range'] = rg(30)
s['mac'] = x
s['rssi'] = '-'+str(rg(40,100))
mac_list.append(s)
mac_outcount = mac_count - mac_incount
if mac_tmp - mac_incount > 4:
for x in range(rg(mac_tmp-mac_incount)):
mac = ''
while True:
mac1 = str(rg(99)).zfill(2)
mac2 = str(rg(99)).zfill(2)
mac = mac0+':'+mac1+':'+mac2
if mac not in time_mac:
break
t = rg(3,20)
time_mac[mac] = time+datetime.timedelta(minutes = t)
s = {}
s['mac'] = mac
s['range'] = rg(30)
s['rssi'] = '-'+str(rg(40,100))
mac_list.append(s)
else:
mac_tmp = mac_incount
mac_outcount = mac_count - mac_tmp
for x in range(mac_outcount):
mac = ''
while True:
mac1 = str(rg(99)).zfill(2)
mac2 = str(rg(99)).zfill(2)
mac = mac0+':'+mac1+':'+mac2
if mac not in time_mac:
break
s = {}
s['mac'] = mac
s['range'] =rg(30,100)
s['rssi'] = '-'+str(rg(40,100))
mac_list.append(s)
temp['time'] = etime
temp['data'] = mac_list[:]
temp['lat'] = str(round(uf(125.293995,125.300265),6))
temp['lon'] = str(round(uf(43.860149,43.860461),6))
temp['location'] = [temp['lon'],temp['lat']]
time = datetime.datetime.strptime(temp['time'],'%a %b %d %H:%M:%S %Y')
temp['@timestamp'] = time.strftime('%Y-%m-%dT%H:%M:%S.000Z')
index = 'sou'
doc_type = 'tz_{number}'.format(number=start_id)
_id = time.strftime('%a %b %d %H:%M:%S %Y')
dic = {'_id':_id,'_index':index,'_type':doc_type,'_source':temp}
data_s.append(dic)
data = temp
import random
from random import uniform as uf
r = 0.5
caiu_dentro = 0
caiu_fora = 0
picalculado = float()
piReal = 3.141
testes = 50000
for x in range(0, testes):
xAleatorio = uf(-0.5, 0.5)
yAleatorio = uf(-0.5, 0.5)
soma = (xAleatorio**2 + yAleatorio**2)**0.5
if soma <= r:
caiu_dentro += 1
else:
caiu_fora += 1
print('\n')
print('The point was within the circle ' + str(caiu_dentro) + ' times.')
print('The point was outside the circle ' + str(caiu_fora) + ' times.')
picalculado = caiu_dentro * 4 / testes
print('\n')
print('The estimated pi was ' + str(picalculado))
print('\n')
print('And that corresponds to an error of ' + str(picalculado - piReal))
def mkjson():
global now_time
global data
global time_mac
global start_id
global data_s
temp = {}
stime = datetime.datetime.strptime(data['time'],
'%a %b %d %H:%M:%S %Y') #获取上一个时间
time = stime + datetime.timedelta(seconds=60) #+10s
now_time = time
etime = time.strftime('%a %b %d %H:%M:%S %Y')
hour = time.hour
mac_count = 0 #mac总数
mac_incount = len(time_mac.keys()) #上一状态店内顾客数
mac_outcount = 0 #未进店mac数
mac_list = [] #新的data列表
mac0 = '00:00:00:00'
mac1 = 0
mac2 = 0
mac_tmp = mac_incount
if hour >= 6 and hour <= 18:
mac_count = rg(80, 120)
mac_tmp = rg(8, 18) #随机出进店人数
elif hour > 18 and hour <= 24:
mac_count = rg(70, 110)
mac_tmp = rg(6, 15)
elif hour >= 0 and hour < 6:
mac_count = rg(40, 80)
mac_tmp = rg(4, 14)
if len(time_mac.keys()) != 0:
for x in time_mac.keys(): #清除超时
if (time_mac[x] - time).total_seconds() <= 0: #超时
time_mac.pop(x)
mac_incount -= 1
else: #添加未超时数据到新列表
s = {}
s['range'] = rg(30)
s['mac'] = x
s['rssi'] = '-' + str(rg(40, 100))
mac_list.append(s)
mac_outcount = mac_count - mac_incount
if mac_tmp - mac_incount > 4: #当随机的进店人数比实际进店人数多4时
for x in range(rg(mac_tmp - mac_incount)): #补足入店人数
mac = ''
while True:
mac1 = str(rg(99)).zfill(2)
mac2 = str(rg(99)).zfill(2)
mac = mac0 + ':' + mac1 + ':' + mac2
if mac not in time_mac:
break
t = rg(3, 20) #设置改mac店内时间
time_mac[mac] = time + datetime.timedelta(minutes=t) #改mac截止时间
s = {}
s['mac'] = mac
s['range'] = rg(30)
s['rssi'] = '-' + str(rg(40, 100))
mac_list.append(s)
else:
mac_tmp = mac_incount
mac_outcount = mac_count - mac_tmp
for x in range(mac_outcount): #未入店顾客
mac = ''
while True:
mac1 = str(rg(99)).zfill(2)
mac2 = str(rg(99)).zfill(2)
mac = mac0 + ':' + mac1 + ':' + mac2
if mac not in time_mac:
break
s = {}
s['mac'] = mac
s['range'] = rg(30, 100)
s['rssi'] = '-' + str(rg(40, 100))
mac_list.append(s)
temp['time'] = etime
temp['data'] = mac_list[:]
temp['lat'] = str(round(uf(125.293995, 125.300265), 6)) #定义经度
temp['lon'] = str(round(uf(43.860149, 43.860461), 6)) #定义维度
temp['location'] = [temp['lon'], temp['lat']]
time = datetime.datetime.strptime(temp['time'], '%a %b %d %H:%M:%S %Y')
temp['@timestamp'] = time.strftime('%Y-%m-%dT%H:%M:%S.000Z') #定义存储时间
index = 'test'
doc_type = 'tz_{number}'.format(number=start_id)
_id = time.strftime('%a %b %d %H:%M:%S %Y')
dic = {'_id': _id, '_index': index, '_type': doc_type, '_source': temp}
data_s.append(dic)
data = temp
if not es.indices.exists(index=index):
es.indices.create(index=index)
def iteration(self, m):
# keep stats
losses = []
scores = []
for game in range(m):
eps_threshold = EPS_END + (EPS_START - EPS_END) * math.exp(
-1. * game / EPS_DECAY)
# randomly set state_0, reset score
game_duration = 0
score = 0
loss = 0
self.rocket.set_state(uf(-100, 100), uf(650, 750), uf(-2.5, 2.5),
uf(-20, 20), uf(-120, -80), uf(-0.6, 0.6))
state_0 = torch.tensor(self.rocket.get_state(),
device=self.device).float()
while not self.rocket.dead:
game_duration += 1
# select next action
if random.random() < eps_threshold:
action_idx = random.randint(0, len(ACTION) - 1)
else:
with torch.no_grad():
out = self.policy_net(state_0)
action_idx = torch.max(out, dim=0)[1].item()
# execute action (state_0, action, reward, state_1, terminal)
action = ACTION[action_idx] # to feed the rocket
transition = self.rocket.update(*action)
score += transition[2]
if self.vis:
if game % 50 == 0:
self.render.frame(self.rocket,
transition[2],
realtime=True)
else:
self.render.clear(game)
# keep transition in memory (state_0, action, reward, state_1, terminal)
self.memory.push(transition, action_idx)
# train minibatch
loss += self.train()
state_0 = torch.tensor(transition[3],
device=self.device).float()
# stats
losses.append(loss / game_duration)
scores.append(score)
mean_loss = sum(scores[-100:]) / len(scores[-100:])
print(
'game: {} score: {:.2f} mean: {:.2f} loss: {:.3f} eps: {:.2f}'.
format(game, score, mean_loss, losses[-1], eps_threshold))
# save state, update target net weights
if (game + 1) % 100 == 0:
dump = {'scores': scores, 'losses': losses}
self.policy_net.save_state()
# pickle.dump(dump, open('runs/' + self.date + '_stats.pkl', 'wb'))
if game % TARGET_UPDATE == 0:
self.target_net.load_state(
deepcopy(self.policy_net.get_state()))