def bet(self, communityData, playerPot: int):
#predefined pre-flop behavior - try to go into flop with low cost
moves = MoveValidator.availableAndLegalMoves(communityData, playerPot,
self.balance)
if Move.BLIND in moves: return Move.BLIND
if moves == [Move.QUIT]: return Move.QUIT
if len(communityData.actions) == 1:
strategy = handToStartCategory(self.hand)
if strategy in [StartCat.PLAYABLE_EXTENT, StartCat.PLAYABLE] or\
(strategy == StartCat.UNTIL_RAISE and sum(1 for x in communityData.actions[-1]if x[1] in [Move.BET, Move.RAISE])):
if Move.CALL in moves: return Move.CALL
if Move.CHECK in moves: return Move.CHECK
return Move.FOLD
playerList = set(x[0] for x in communityData.actions[-1]
if x[0] != self.name)
commVal = list(
map(lambda x: CardVal.checkHighCard([x]) / 1.0,
communityData.communityCards))
predStr = []
for idx, player in enumerate(playerList):
ans = tf.convert_to_tensor(
tuple([[
*commVal[:3 + len(communityData.actions) - 2],
countCalls(player, communityData.actions[-1]),
countBets(player, communityData.actions[-1]), idx / 1.0,
len(playerList) / 1.0
]]))
predStr.append(*ann[len(communityData.actions) - 2].predict(ans))
if len(predStr) > 0:
ans = tf.convert_to_tensor(
tuple([[
*[CardVal.checkHighCard([x]) / 1.0 for x in self.hand],
*commVal,
max(predStr)[0]
]]))
if dec_ann[len(communityData.actions) - 2].predict(ans) > 0.9:
if random.choice([True, False]):
if Move.BET in moves: return Move.BET
elif Move.RAISE in moves: return Move.RAISE
else:
if Move.CALL in moves: return Move.CALL
elif Move.CHECK in moves: return Move.CHECK
if Move.CHECK in moves: return Move.CHECK
return Move.FOLD if Move.FOLD in moves else Move.QUIT
def bet(self, communityData, playerPot: int):
#predefined pre-flop behavior - try to go into flop with low cost
moves = MoveValidator.availableAndLegalMoves(communityData, playerPot,
self.balance)
if len(communityData.actions) == 1:
if Move.CALL in moves: return Move.CALL
elif Move.CHECK in moves: return Move.CHECK
elif Move.BLIND in moves: return Move.BLIND
return Move.QUIT
playerList = set(x[0] for x in communityData.actions[-1]
if x[0] != self.name)
commVal = list(
map(lambda x: CardVal.checkHighCard([x]) / 1.0,
communityData.communityCards))
predStr = []
for idx, player in enumerate(playerList):
ans = tf.convert_to_tensor(
tuple([[
*commVal[:3 + len(communityData.actions) - 2],
countCalls(player, communityData.actions[-1]),
countBets(player, communityData.actions[-1]), idx / 1.0,
len(playerList) / 1.0
]]))
predStr.append(ann[len(communityData.actions) - 2].predict(ans))
if len(predStr) > 0 and\
max(predStr) > CardVal.combination(communityData.communityCards + self.hand):
return Move.FOLD
if random.choice([True, False]):
if Move.BET in moves: return Move.BET
elif Move.RAISE in moves: return Move.RAISE
else:
if Move.CALL in moves: return Move.CALL
elif Move.CHECK in moves: return Move.CHECK
if Move.CHECK in moves: return Move.CHECK
return Move.FOLD if Move.FOLD in moves else Move.QUIT
def countActions(l, actions):
return sum(1 for x in l if x in actions) / 1.0
with open('/home/szymon/inne/ips/PokerHandsDataset/hands_valid.json',
'r') as f:
features = ([], [], [])
labels = ([], [], [])
line = f.readline()
while line:
info = json.loads(line)
comm = stringsToCards(info['board'])
numOfPlayers = [x['num_players'] for x in info['pots'][1:]]
commPts = list(
map(lambda x: CardValidator.checkHighCard([x]) / 1.0, comm))
points = [[], [], []]
winners = []
for player in info['players']:
if player['winnings'] > 0:
winners.append(player['user'])
for idx in range(3):
points[idx].append([
player['user'],
CardValidator.combination(
stringsToCards(player['pocket_cards']) +
comm[:3 + idx]) / 1.0
])
for player in info['players']:
hand = [