def main():
print "Entity Linking System in Web Tables with Multiple Linked Knowledge Bases"
print "Version 2.1"
print "Copyright @2017/3/1 Shengjia Yan. All Rights Reserved."
# mark()
# preprocess()
# candidate_generation()
entity_disambiguation()
result()
def QLearning(num_episodes, e, gamma, lr, Q, GenerateAction1):
for epi in range(0, num_episodes):
i = 0
done = False
reward = 0
action1 = {}
action2 = {}
Grade1, Grade2, player1, player2 = Grade()
[level1, rank1] = Grade1
# if level1 > 1:
# action111 = (1, 1, 1, 1, 1)
# if level1 == 1:
# action111 = (1, 0, 0)
[level, rank] = GradetheAgent()
winnerp = judge(Grade1, Grade2)
action = (0, 1, 2, 3)
#####################
#taolu
if GenerateAction1 == 1:
action111 = genact1(level1, rank1)
if GenerateAction1 == 2:
action111 = genact2(level1, rank1)
if GenerateAction1 == 3:
action111 = genact3(level1, rank1)
#action1[0] = random.choice(action)
action1[0] = action111[0]
pot = 30
R = 0
delta = 10
if action1[0] == 0 or action1[0] == 3:
done = True
action2[0] = 0
#reward = result(action1, action2, winnerp)
if action1[0] == 1:
pot = 10
delta = 10
if action1[0] == 2:
pot = 20
delta = 20
state = state2[pot][R][delta][level][rank]
while not done:
if action1[i] == 0 or action1[i] == 3:
reward = result(action1, action2, winnerp)
Q[state][0] = Q[state][0] + lr * reward
Q[state][1] = Q[state][1] + lr * reward
Q[state][2] = Q[state][2] + lr * reward
Q[state][3] = Q[state][3] + lr * reward
break
if action1[i] == 1 or action1[i] == 2:
a = random.random()
if a < 1 - 4 / 3 * e:
action2[i] = np.argmax(Q[state])
else:
action2[i] = random.choice(action)
#action1[i+1] = random.choice(action)
action1[i + 1] = action111[i + 1]
QA = np.zeros(4)
#if (action2[i] == 0 or action2[i] == 3) or R == 4:
if (action2[i] == 0 or action2[i] == 3) or R == 2:
reward = result(action1, action2, winnerp)
Q[state][action2[i]] = Q[state][action2[i]] + lr * reward
break
if action2[i] == 1:
if action1[i + 1] == 1:
pot = pot + 2 * delta
if action1[i + 1] == 2:
pot = pot + 3 * delta
delta = 2 * delta
if action2[i] == 2:
if action1[i + 1] == 1:
pot = pot + 4 * delta
delta = 2 * delta
if action1[i + 1] == 2:
pot = pot + 6 * delta
delta = 4 * delta
statep = state2[pot][R][delta][level][rank]
for actionp in range(0, 4):
QA[actionp] = Q[statep][actionp]
Q[state][action2[i]] = Q[state][action2[i]] + lr * (
reward + gamma * max(QA) - Q[state][action2[i]])
state = statep
R = R + 1
i = i + 1
# if epi > 999900:
# print(action2)
return Q
# print('cards of Player1:')
# print(player1)
[level, rank] = Grade2
winnerp = judge(Grade1, Grade2)
action = (0, 1, 2, 3)
action1 = {}
action2 = {}
pot = 20
delta = 10
#for R in range(0, 5):
for R in range(0, 3):
action1[R] = random.randint(0, 3)
if action1[R] == 0 or action1[R] == 3:
action2[R] = 0
reward = result(action1, action2, winnerp)
break
if action1[R] == 1:
pot = pot + delta
state = state2[pot][R][delta][level][rank]
action2[R] = np.argmax(Q[state])
if action2[R] == 1:
pot = pot + delta
if action2[R] == 2:
pot = pot + 2 * delta
delta = 2 * delta
if action1[R] == 2:
pot = pot + 2 * delta
delta = 2 * delta
state = state2[pot][R][delta][level][rank]
from result import *
import pickle
for depth in [2,3,4]:
for branches in [11]:
r=result(s,branches,depth)
p=r.finalResult
print depth,branches,p[0],p[1][0],p[1][1],p[1][2],p[1][3]
del p
del r
gc.collect()
from result import *
import pickle
for depth in [2, 3, 4]:
for branches in [11]:
r = result(s, branches, depth)
p = r.finalResult
print depth, branches, p[0], p[1][0], p[1][1], p[1][2], p[1][3]
del p
del r
gc.collect()
hheaders1 = ['alumno']
hheaders1.extend(list(np.unique(vr._get_('videos','observer._id_'))))
vheaders1 = ['segundos']
times2 = vr.timesFor('weekdays')
hheaders2 = ['dia']
hheaders2.extend(list(np.unique(vr._get_('videos','weekday()'))))
vheaders2 = ['segundos']
times3 = vr.timesFor('blocks')
hheaders3 = ['bloque']
hheaders3.extend(list(np.unique(vr._get_('videos','block'))))
vheaders3 = ['segundos']
times4 = vr.timesFor('subjects')
hheaders4 = ['materia']
hheaders4.extend(list(np.unique(vr._get_('videos','subject'))))
vheaders4 = ['segundos']
times5 = vr.timesFor('observers','subjects')
hheaders5 = ['alumno\\materia']
hheaders5.extend(list(np.unique(vr._get_('videos','subject'))))
vheaders5 = list(np.unique(vr._get_('videos','observer._id_')))
hheaders = [hheaders1,hheaders2,hheaders3,hheaders4,hheaders5]
vheaders = [vheaders1,vheaders2,vheaders3,vheaders4,vheaders5]
times = [times1,times2,times3,times4,times5]
res = result('tiempos de grabacion', hheaders, vheaders, times)
res.toExcel('tiempos.xls')