def update_river(self, table):
board = HandStat(table)
self.vec_cards['river'] = board.stat( features=river_features,
hole_cards=self.cards )
my = HandStat( self.cards + table )
self.vec_cards['my river'] = my.stat( features=my_river_features )
self.stage=3
def update_turn(self, table):
board = HandStat(table)
self.vec_cards['turn'] = board.stat( features=turn_features,
hole_cards=self.cards )
my = HandStat( self.cards + table )
self.vec_cards['my turn'] = my.stat( features=my_postflop_features )
self.stage=2
def update_flop(self, table):
#table is a list of Cards
board = HandStat(table)
self.vec_cards['flop'] = board.stat( features=flop_features,
hole_cards=self.cards )
my = HandStat( self.cards + table )
self.vec_cards['my flop'] = my.stat( features=my_postflop_features )
self.stage=1
def __init__(self):
self.net = UnbiasedNet.NeuralNet(fw.n_in,
fw.n_hidden,
fw.n_out,
randomInit=False)
self.status = fw.StatStatus()
self.name = "CallingStation"
def update_river(self, table):
board = HandStat(table)
self.vec_cards['river'] = board.stat(features=river_features,
hole_cards=self.cards)
my = HandStat(self.cards + table)
self.vec_cards['my river'] = my.stat(features=my_river_features)
self.stage = 3
def update_turn(self, table):
board = HandStat(table)
self.vec_cards['turn'] = board.stat(features=turn_features,
hole_cards=self.cards)
my = HandStat(self.cards + table)
self.vec_cards['my turn'] = my.stat(features=my_postflop_features)
self.stage = 2
def update_flop(self, table):
#table is a list of Cards
board = HandStat(table)
self.vec_cards['flop'] = board.stat(features=flop_features,
hole_cards=self.cards)
my = HandStat(self.cards + table)
self.vec_cards['my flop'] = my.stat(features=my_postflop_features)
self.stage = 1
def update_preflop(self, cards):
self.cards = list(cards) # needed later
hand = HandStat(cards=cards)
self.vec_cards['my preflop'] = hand.stat(features=my_preflop_features)
next = self.status.check_fold()
action = "CheckFold"
self.status = next.copy()
#update the other guy's status vector resulting from your act
player2.status.vec_act[stage][1] = self.status.vec_act[stage][0]
player2.status.vec_act[stage][2] = self.status.vec_act[stage][2]
player2.status.stage = self.status.stage
return action
import pickle
if 1:
# auto= pickle.load(open("player.p", "rb"))
net = UnbiasedNet.NeuralNet(fw.n_in,
fw.n_hidden,
fw.n_out,
alpha=0.02,
lamb=0.9,
randomInit=True)
auto = fw.MyAutoPlayer(net, name="superbot")
cs = Calling_station()
auto.train(50000, cs, debug=0)
pickle.dump(auto, open("player2.p", "wb"))
if 1:
cs = Calling_station()
auto = pickle.load(open("player2.p", "rb"))
result = []
for i in range(40):
result.append(auto.compete(cs, 5000, debug=0))
print result
def update_preflop(self, cards):
self.cards = list(cards) # needed later
hand = HandStat( cards=cards )
self.vec_cards['my preflop'] = hand.stat( features=my_preflop_features )
debug=debug)
game.endRound()
if debug:
print "End of one hand. The winning is", result[1], "\n"
start_cash += result[1]
return start_cash
if __name__ == "__main__":
ALPHA = 0.0005
LAMB = 0.6
n_train = 10
net1 = UnbiasedNet.NeuralNet(fw.n_in,
fw.n_hidden,
fw.n_out,
randomInit=True,
alpha=ALPHA,
lamb=LAMB)
auto1 = fw.MyAutoPlayer(net1, name="auto1", frenzy=True)
net2 = UnbiasedNet.NeuralNet(fw.n_in,
fw.n_hidden,
fw.n_out,
randomInit=True,
alpha=ALPHA,
lamb=LAMB)
auto2 = fw.MyAutoPlayer(net2, name="auto2", frenzy=True)
big_small = Big_small_blind(auto1, auto2, frenzy=True)
import calling_station
csbot = calling_station.Calling_station()
big_small.train(n_train, csbot, frenzy=True, debug=1)
big_small.compete(csbot)