def negamax(candidates, role, deep, alpha, beta):
board.currentSteps = []
for i in range(len(candidates)):
p = candidates[i]
board.put(p, role)
steps = [p]
v = r(deep - 1, -beta, -alpha, R.reverse(role), 1, steps[:], 0)
v.score *= -1
alpha = max(alpha, v.score)
board.remove(p)
p.v = v
return alpha
def main():
e = False
winname = []
win = 0
los = 0
while True:
if e:
break
board.init(15)
#print(board.board)
game_id = joinGame()
size = -1
while True:
time.sleep(6)
gjson = check_game(game_id)
if (gjson['is_success'] == True):
if gjson['current_turn'] == '我是小号':
#print("(len(gjson['board']):"+str(len(gjson['board'])))
p = Point.point
last_step = gjson['last_step']
if (len(last_step) != 0):
p.x = toInt(last_step[0])
p.y = toInt(last_step[1])
board.put(p, R.hum)
if (len(gjson['board']) == 0):
p.x = 7
p.y = 7
#elif len(gjson['board'])==2:
# p.x = toInt(last_step[0])+1
# p.y = toInt(last_step[1])+1
else:
s = time.time()
p = m(R.com, 7)
print('spend time:' + str(time.time() - s))
if playGame(game_id,
toChar(p.x) +
toChar(p.y))['is_success'] == True:
board.put(p, R.com)
if gjson['winner'] != 'None':
winname.append(gjson['winner'])
if gjson['winner'] == '我是小号':
win += 1
else:
los += 1
e = True
print('win count:' + str(win) + ' los count:' + str(los))
print('winplyers:' + str(winname))
break
else:
break
def cs():
p = Point.point
if (len(board.allSteps) > 1):
s = time.time()
p = m(R.com, 6)
print(time.time() - s)
board.put(p, R.com)
else:
p.x = board.currentSteps[0].x + 1
p.y = board.currentSteps[0].y + 1
cp(p.x, p.y)
if 'score' in p.__dict__:
return p.x, p.y, p.score
else:
return p.x, p.y, 0
def hp(x, y):
p = Point.point
p.x = x
p.y = y
board.put(p, R.hum)
def cp(x, y):
p = Point.point
p.x = x
p.y = y
board.put(p, R.com)
def r(deep, alpha, beta, role, step, steps, spread):
global Cache
global cacheGet
if (board.zobrist.code in Cache):
c = Cache[board.zobrist.code]
if (c.deep >= deep):
cacheGet += 1
score = Score()
score.score = c.score.score
score.steps = steps
score.step = step + c.score.step
score.c = c
return score
#else:
# if (mymath.greatOrEqualThan(c.score.score, SCORE.FOUR) or mymath.littleOrEqualThan(c.score.score, -SCORE.FOUR)):
# cacheGet +=1
# return c.score
_e = board.evaluate(role)
leaf = Score()
leaf.score = _e
leaf.step = step
leaf.steps = steps
global count
count += 1
if deep <= 0 or mymath.greatOrEqualThan(
_e, SCORE.FIVE) or mymath.littleOrEqualThan(_e, -SCORE.FIVE):
return leaf
best = Score()
global MIN
best.score = MIN
best.step = step
best.steps = steps
points = board.gen(role, step > 1 if board.count > 10 else step > 3,
step > 1)
if not len(points):
return leaf
for i in range(len(points)):
p = points[i]
board.put(p, role)
_deep = deep - 1
_spread = spread
if (_spread < 1):
if ((role == R.com and p.scoreHum >= SCORE.FIVE)
or (role == R.hum and p.scoreCom >= SCORE.FIVE)):
_deep += 2
_spread += 1
_steps = steps[:]
_steps.append(p)
v = r(_deep, -beta, -alpha, R.reverse(role), step + 1, _steps, _spread)
v.score *= -1
board.remove(p)
if (v.score > best.score):
best = v
alpha = max(best.score, alpha)
global ABcut
if (mymath.greatOrEqualThan(v.score, beta)):
ABcut += 1
v.score = MAX - 1
v.abcut = 1
return v
cache(deep, best)
return best
def cs():
if (len(board.allSteps) > 1):
p = m(R.com, 8)
board.put(p, R.com)
test(board.board)
