def run_basic_strategy_game(manual=False):
deck = Deck()
player_hand = deck.draw(3)
dealer_hand = deck.draw(3)
action_computer = decision_basic_strategy(player_hand, dealer_hand)
result_computer = determine_payout(player_hand, dealer_hand,
action_computer)
# let the user try to play (if requested). user can play after the computer
# since we are not holecarding
if manual:
print(f"\n\n\n\n===========[New Three Card Poker Hand]===========")
print(f"Player Hand: {player_hand}")
action_human = bool(int(input("0-Fold or 1-Call >>> ")))
result_human = determine_payout(player_hand, dealer_hand, action_human)
sort_hand(player_hand)
sort_hand(dealer_hand)
print(f"\n----------- Summary -----------")
print(f"Player Hand: {player_hand}")
print(f"Dealer Hand: {dealer_hand}")
print(f"Computer's Decision: {action_string(action_computer)}")
print(
f"Result: Computer got {result_computer} units and you got {result_human} units"
)
return (result_computer, result_human)
return result_computer
def is_good(board_cards=[], hand_cards=['AH', 'KD']):
if (len(board_cards) + len(hand_cards) == 0):
return False
board = []
for x in board_cards:
c = Card.new('{0}{1}'.format(x[0], x[1].lower()))
board.append(c)
hand = []
for x in hand_cards:
c = Card.new('{0}{1}'.format(x[0], x[1].lower()))
hand.append(c)
evaluator = Evaluator()
deck = Deck()
random_cards = deck.draw(5 - len(board) - len(hand))
score = evaluator.evaluate(board + random_cards, hand)
rank_class = evaluator.get_rank_class(score)
print_cards(board + random_cards + hand, rank_class)
if (rank_class < 9):
return True
return False
def prepare_next_hand(self, update_action_data=False):
self.board = []
self.current_pot = 0
# Reorder players by moving first to back
player_0 = self.players[0]
self.players.pop(0)
self.players.append(player_0)
self.reset_player_position_indexes()
# Set players with no stack to inactive
for p in self.players:
if p.stack <= 0:
p.active = False
else:
p.active = True
# self.players = [p for p in self.players if p.active]
# Remove player hole cards
for p in self.players:
if update_action_data:
p.update_action_data_net_stack(self.hand_idx)
p.hand = None
p.stage_actions = []
p.hand_actions = []
p.prev_stack = p.stack
p.all_in = False
# p.hand_action_count = 0
# Reset the deck
self.deck = Deck()
self.hand_idx += 1
def run_holecarding_strategy_game(manual=False):
deck = Deck()
player_hand = deck.draw(3)
dealer_hand = deck.draw(3)
dealer_holecard = dealer_hand[0]
# now the computer can play out the hand (and possibly sort dealer's hand)
action_computer = decision_holecarding(player_hand, dealer_hand)
result_computer = determine_payout(player_hand, dealer_hand,
action_computer)
# print hand summary
if manual:
print(f"\n\n\n\n===========[New Three Card Poker Hand]===========")
print(f"Player Hand: {player_hand}")
print(f"Dealer's Holecard: {dealer_holecard}")
action_human = bool(int(input("0-Fold or 1-Call >>> ")))
result_human = determine_payout(player_hand, dealer_hand, action_human)
sort_hand(player_hand)
sort_hand(dealer_hand)
print(f"\n----------- Summary -----------")
print(f"Player Hand: {player_hand}")
print(f"Dealer Hand: {dealer_hand}")
print(f"Computer's Decision: {action_string(action_computer)}")
print(
f"Result: Computer got {result_computer} units and you got {result_human} units"
)
return (result_computer, result_human)
return result_computer
def __init__(self, preflop_tight_loose_threshold, aggresive_passive_threshold, bet_tolerance): self.preflop_tight_loose_threshold = preflop_tight_loose_threshold self.aggresive_passive_threshold = aggresive_passive_threshold self.bet_tolerance = bet_tolerance # deuces self.evaluator = Evaluator() self.deck = Deck()
def calc_hand_strength(self, game):
score = [0, 0]
table = game.table[:]
for i in range(0, 500):
deck = Deck()
new = 0
player1_hand = self.hand
player2_hand = deck.draw(2)
if table == []:
table = deck.draw(3)
while len(set(player1_hand + player2_hand + table)) < len(player1_hand) + len(player2_hand) + len(table):
player2_hand = deck.draw(2)
table = deck.draw(3)
new = 1
if len(player1_hand) + len(player2_hand) + len(table) == len(set(player1_hand + player2_hand + table)):
p1_score = game.evaluator.evaluate(table, player1_hand)
p2_score = game.evaluator.evaluate(table, player2_hand)
if p1_score < p2_score:
score[0] += 1
score[1] += 1
elif p2_score < p1_score:
score[1] += 1
else:
score[0] += 1
score[1] += 2
if new == 1:
table = []
strength = score[0] / score[1]
return strength
def test_draws_are_consistent(self):
random.seed(0)
hand = [Deck() for _ in range(5)]
df_all = pd.DataFrame({'all': [deck.draw(9) for deck in hand]})
expected = [[[
81922, 33560861, 139523, 16783383, 147715, 69634, 164099, 1053707,
16795671
]],
[[
16783383, 4212241, 295429, 69634, 81922, 73730,
134236965, 8394515, 557831
]],
[[
266757, 1082379, 134236965, 557831, 16787479, 67127839,
529159, 134253349, 1053707
]],
[[
33564957, 8423187, 1065995, 33589533, 98306, 268471337,
2114829, 16812055, 67119647
]],
[[
16795671, 529159, 135427, 16783383, 266757, 2106637,
279045, 8423187, 8394515
]]]
self.assertEqual(df_all.values.tolist(), expected)
random.seed(0)
hand = [Deck() for _ in range(5)]
df_board = pd.DataFrame({'board': [deck.draw(5) for deck in hand]})
expected = [[[81922, 33560861, 139523, 16783383, 147715]],
[[16783383, 4212241, 295429, 69634, 81922]],
[[266757, 1082379, 134236965, 557831, 16787479]],
[[33564957, 8423187, 1065995, 33589533, 98306]],
[[16795671, 529159, 135427, 16783383, 266757]]]
self.assertEqual(df_board.values.tolist(), expected)
def cross_over_aux(self,chromosome1,chromosome2,position):
cross_over = 0
crossed = False
deck = Deck()
hand = deck.draw(5)
new_chromosome = Player(hand)
for gene in range(NUMBER_OF_PLAYERS-1):
if (not(crossed)):
if (position == cross_over):
crossed = True
#Mutation
random_mutation = random.randint(0,100)
if (0 < random_mutation <= MUTATION_PROBABILITY*100):
mutated = self.mutate()
new_chromosome.hand[cross_over] = mutated
# new_chromosome.hand[cross_over] = chromosome1.hand[cross_over]
else:
new_chromosome.hand[cross_over] = chromosome1.hand[cross_over]
else:
#Mutation
random_mutation = random.randint(0,100)
if (0 < random_mutation <= MUTATION_PROBABILITY*100):
mutated = self.mutate()
new_chromosome.hand[cross_over] = mutated
# new_chromosome.hand[cross_over] = chromosome1.hand[cross_over]
else:
new_chromosome.hand[cross_over] = chromosome2.hand[cross_over]
cross_over += 1
return new_chromosome
def run(number):
"""
This function generates a population sample with size sample.
It also computes probabilities for each hand, considering a
frequentist approach.
Arguments:
- number (int): run ID
Returns:
- stats (str): returns a JSON formatted string containing
probabilities for each poker hand
"""
print 'starting run #{0}'.format(number)
evaluator = Evaluator()
poker_hands = defaultdict(int)
for _ in range(SAMPLE_SIZE):
deck = Deck()
p1_hand = deck.draw(5)
p1_score = evaluator.evaluate(p1_hand, [])
if p1_score == 1: # just a little hack
poker_hands['Royal Flush'] += 1
else:
p1_class = evaluator.get_rank_class(p1_score)
poker_hands[evaluator.class_to_string(p1_class)] += 1
stats = dict((k, round(float(v)/SAMPLE_SIZE, 7))
for k, v in poker_hands.items())
return json.dumps(stats)
def sample_win_probability_dumb(board,
value_to_beat,
deck,
left_to_deal,
nsamples=100):
nwins = 0
cards = deck.cards
copied_deck = Deck()
for _ in xrange(nsamples):
shuffle(cards)
copied_deck.cards = list(cards)
won = False
decided = False
best_value = value_to_beat
chosen_value = 10**10
for x in range(left_to_deal):
hand = copied_deck.draw(2)
value = evaluator.evaluate(board, hand)
if value < value_to_beat and not decided:
# have to decide whether to stay!
win_prob = chance_to_win_given_choice(board, value, deck,
left_to_deal - x - 1)
if win_prob > 0.5:
chosen_value = value
decided = True
best_value = min(best_value, value)
if chosen_value < best_value:
nwins += 1
return nwins * 1.0 / nsamples
def possibility_of_getting_burnt(board,
chosen_value,
deck,
left_to_deal,
nsamples=100):
# the only way that keeping the hand with a <50% chance of winning
# is the correct move is if there is at least some possibility that
# there will be *two* hands that beat you, and moreover they come in
# the wrong order
if left_to_deal <= 1:
# you IDIOT, of course keep your hand
return 0
nburns = 0
cards = deck.cards
copied_deck = Deck()
for _ in xrange(nsamples):
shuffle(cards)
copied_deck.cards = list(cards)
got_beat = False
value_to_beat = -1
for x in range(left_to_deal):
hand = copied_deck.draw(2)
value = evaluator.evaluate(board, hand)
if got_beat and value < value_to_beat:
nburns += 1
break
if value < chosen_value:
got_beat = True
value_to_beat = value
return nburns * 1.0 / nsamples
class Table:
'''
Creates a poker table containing the deck, and a means to store the cards.
'''
def __init__(self):
self.deck = Deck()
self.cards = []
self.pot = 0
self.ante = 0
'''
Adds a card to the cards on the table.
'''
def addCard(self, card):
self.cards.append(card)
'''
Adds to the current pot on the table.
'''
def addToPot(self, amt):
self.pot += amt
'''
Gets the current pot.
'''
def getPot(self):
return self.pot
'''
Gets the community cards on the table.
'''
def getCards(self):
return self.cards
'''
Draws from the deck.
'''
def draw(self):
return self.deck.draw()
def setAnte(self, ante):
self.ante = ante
def getAnte(self):
return self.ante
'''
Resets the table.
'''
def reset(self):
self.deck.shuffle()
del self.cards[:]
self.ante = 0
self.pot = 0
def sample_win_probability(board,
value_to_beat,
deck,
left_to_deal,
nsamples=50):
pwin = 0.
cards = deck.cards
copied_deck = Deck()
for _ in xrange(nsamples):
shuffle(cards)
copied_deck.cards = list(cards)
new_hand = copied_deck.draw(2)
value = evaluator.evaluate(board, new_hand)
if value > value_to_beat:
# the new hand is worse, so no decision to be made
if left_to_deal > 1:
# just burn the cards and keep going
pwin += sample_win_probability(board, value_to_beat,
copied_deck, left_to_deal - 1)
elif left_to_deal == 1:
# this was our last chance, we lost
pwin += 0
else:
if left_to_deal == 1:
# this was our last hand. We won!
pwin += 1
else:
# we have a choice. What do we do??
prob_if_stayed, prob_burn = chance_to_win_given_choice(
board,
value,
copied_deck,
left_to_deal - 1,
return_burns=True)
# we have the inequality
# P(there is a better hand) - P(you get "burnt")
# < P(win if you pass) < P(there is a better hand)
# also,
# P(there is a better hand) = 1 - P(win if you stay)
# If P(win if you pass) < P(win if you stay) then you should stay
# and if P(win if you pass) > P(win if you stay) then you should continue
if prob_if_stayed > 0.5:
# definitely should stay
pwin += prob_if_stayed
continue
if prob_burn <= 0.1:
# if burns are pretty rare, then let's just say that
# the win probability is well approximated by the
# "dumb" strategy.
prob_if_passed = sample_win_probability_dumb(
board, value, copied_deck, left_to_deal - 1)
else:
prob_if_passed = sample_win_probability(
board, value, copied_deck, left_to_deal - 1)
pwin += max(prob_if_stayed, prob_if_passed)
return pwin * 1. / nsamples
def build(self, testMode=True):
"""
Initiate objects and views.
"""
''' init game objects '''
self.deck = Deck()
self.evaluator = Evaluator()
self.player = []
self.player.append(Player(0))
self.player.append(Player(1))
# board stands for public cards on board
self.board = Board()
# In test mode, both player select right-most cards for the turn automatically
self.testMode = testMode
''' create view objects '''
# Scatter that can be rotated to display players
scatter_bot = ScatterLayout(do_rotation=False,
do_translation=False,
do_scale=False,
size_hint=(1, 1),
pos_hint={
'x': 0,
'y': 0
},
rotation=0)
# For player on top, the widget rotates 180 degree
scatter_top = ScatterLayout(do_rotation=False,
do_translation=False,
do_scale=False,
size_hint=(1, 1),
pos_hint={
'x': 0,
'y': 0
},
rotation=180)
box = PlayerDeck()
box2 = PlayerDeck()
publicArea = PublicArea()
box.build(self, "player1", 0, self.testMode)
box2.build(self, "player2", 1, self.testMode)
publicArea.build()
scatter_bot.add_widget(box)
scatter_top.add_widget(box2)
self.add_widget(scatter_bot)
self.add_widget(scatter_top)
self.add_widget(publicArea)
# register id of view objects
self.ids[box.id] = box
self.ids[box2.id] = box2
self.ids[publicArea.id] = publicArea
def callAI(self, state): #CHANGE THIS FUNCTION. NEVER REFERENCE OTHER PLAYER'S CARDS. You are not allowed to cheat! (obviously) e.g. If we see curState.players[x].curHand, that's unacceptable. MAX_SCORE= 7462 score = 0 # we evaluate the score of the current situation and based on the estimated score, pick an acion # We randomly generate the board N times and get the average score N = 5 i = 0 if curState.curStage == "river": score = score + evaluator.evaluate(curState.board, self.curHand) else: while i < N: my_deck = Deck() my_board = my_deck.draw(0) if curState.curStage == "preflop": exclude = copy.deepcopy(self.curHand) my_board = self.draw(5, exclude, my_deck) elif curState.curStage == "flop": exclude = copy.deepcopy(self.curHand) + copy.deepcopy(curState.board) my_board = curState.board + self.draw(2, exclude, my_deck) elif curState.curStage == "turn": exclude = copy.deepcopy(self.curHand) + copy.deepcopy(curState.board) my_board = curState.board + self.draw(1, exclude, my_deck) score = score + evaluator.evaluate(self.curHand, my_board) i = i + 1 score = score / N #raiseAmount = random.randint(0, 100) #maxbid = max(raiseAmount, random.randint(0, 100)) #if maxbid > self.money: #do not remove # maxbid = self.money #if raiseAmount > self.money: #do not remove # raiseAmount = self.money #possibleActions = ["check", ["raise", raiseAmount]] #can only check or raise, since only one action is processed. Fold if max bid is lower than the biggest raise. #There are 7462 scores in total (smaller is better). We choose an action based on the estimated score if score > MAX_SCORE * 9 / 10: # the score is too high, choose fold raiseAmount = 0 action = ["raise", 0] maxbid = 0 # maxbid = 0, means fold elif score > MAX_SCORE / 2: action = ["check"] maxbid = 0 else: raiseAmount = random.randint(0, self.money * (1 - score / MAX_SCORE) / 2) maxbid = max(raiseAmount, random.randint(0, self.money * (1 - score / MAX_SCORE)) ) action = ["raise",raiseAmount ] return [ action, maxbid ]
def __init__(self, player_list, chips, blinds):
self.player_list = player_list
self.player_num = len(player_list)
#self.hands = [[] for i in range(len(player_list))]
self.deck = Deck()
self.evaluator = Evaluator()
self.blinds = blinds
self.chips = chips
self.pot = [0 for x in range(0,self.player_num)]
self.table_chips = 0
self.raise_counter = 0
self.table = []
self.strengths =[[], []]
def setup(n, m):
deck = Deck()
boards = []
hands = []
for i in range(n):
boards.append(deck.draw(m))
hands.append(deck.draw(2))
deck.shuffle()
return boards, hands
def is_win(hand, board, player_num):
'''
模拟一局游戏，判断自己是否获胜
:param hand:
:param board:
:param player_num:
:return: True or False
'''
# 生产一副新牌
deck = Deck()
# 去除目前自己的手牌
deck.cards.remove(hand[0])
deck.cards.remove(hand[1])
# 去除目前已知桌牌
for board_card in board:
deck.cards.remove(board_card)
# 随机获取剩余桌牌
new_board_cards = deck.draw(5 - len(board))
# 如果 （5 - len(board)） 等于 1，那么取出的会是一个int
if type(new_board_cards) == int:
new_board_cards = [new_board_cards]
# 5 张桌面牌
draw_board = board + new_board_cards
# 计算自己牌的大小，值越小牌越好
my_cards_evaluate = evaluator.evaluate(draw_board, hand)
# 最大值是7432
other_cards_evaluate = 10000
for i in range(player_num - 1):
# 为其他玩家随机发两张牌
player_cards = deck.draw(2)
# 机器玩家牌的大小
random_other_cards_evaluate = evaluator.evaluate(draw_board, player_cards)
# 更新其他玩家最大牌
if random_other_cards_evaluate < other_cards_evaluate:
other_cards_evaluate = random_other_cards_evaluate
# 如果已有一个玩家的牌大于自己，判断自己输
if other_cards_evaluate < my_cards_evaluate:
return False
# 没有玩家牌大于自己，判定自己赢
return True
def build_dataframe(nb_hands=100, nb_players=2):
decks = [Deck() for _ in range(nb_hands)]
df_board = pd.DataFrame({'board': [deck.draw(5) for deck in decks]})
df_players = pd.concat([
pd.DataFrame({'player': [deck.draw(2) for deck in decks]})
for _ in range(nb_players)
],
axis=1,
keys=range(nb_players))
df_players = df_players.swaplevel(0, 1, axis=1)
evaluator = Evaluator()
df_scores = pd.concat([
df_board.join(df_players.xs(i, level=1, axis=1)).apply(
lambda x: evaluator.evaluate(x['board'], x['player']),
axis=1).to_frame('score')
for i in df_players.columns.get_level_values(1)
],
axis=1,
keys=df_players.columns.get_level_values(1))
df_scores = df_scores.swaplevel(0, 1, axis=1)
return df_board, df_players, df_scores
def __init__(self, lobby, minBetInit, startingCash, minRaiseInit):
self.lobby = lobby
self.startingCash = startingCash
self.roundObject = None
self.cardRankLUT = dict()
self.cardRankLUT = {
0: '2',
1: '3',
2: '4',
3: '5',
4: '6',
5: '7',
6: '8',
7: '9',
9: 'J',
8: 'T',
10: 'Q',
11: 'K',
12: 'A'
}
self.cardSuitLUT = dict()
self.cardSuitLUT = {1: 's', 2: 'h', 4: 'd', 8: 'c'}
self.roundCounter = 0
self.players = []
self.endGameUsers = []
self.unshuffledDeck = Deck()
self.currentPlayerIndex = 0
self.minBet = minBetInit
self.minRaise = minRaiseInit
def rest_of_the_deck(self):
"""Return a list with the rest of the cards in the deck."""
full_deck = Deck.GetFullDeck()
return [
elem for elem in full_deck
if elem not in (self._cards + self._board._cards)
]
def __init__(self, bot, name='kiwi'):
self.players = []
self.name = name
self.deck = Deck()
self.visible_cards = []
self.bot = bot
self.potpies = 0
def __init__(self, player_list, chips, blinds, num_hands=100):
self.player_list = player_list
self.player_num = 2
self.num_hands = num_hands
self.deck = Deck()
self.evaluator = Evaluator()
self.blinds = blinds
self.chips = chips
self.pot = [0 for x in range(0, 2)]
self.table_chips = 0
self.raise_counter = 0
self.table = []
self.bet_round = 0
self.strengths =[[], []]
self.last_bets = [None,None]
self.times = dict([('main',{'total':0,'count':0}),('strength',{'total':0,'count':0})]+[(player.name,{'total':0,'count':0}) for player in player_list])
def __init__(self, gameObject): self.pot = 0 self.sidePot = 0 self.currentMinBet = gameObject.minBet self.roundPlayers = gameObject.players self.playerStatus = [] self.playerHandScores = [] self.playerHandClasses = [] self.board = [] self.deck = Deck() #self.deck = random.sample(gameObject.unshuffledDeck, 52) #deck shuffle -> uporablja Fisher-Yates O(n) for p in self.roundPlayers: #empty player hands p.hand = [] p.currentBet=0 self.allIn=False self.allInDifference=0
def __init__(self, n_players):
self.deck = Deck()
self.hands = [
list(x)
for x in zip(self.deck.draw(n_players), self.deck.draw(n_players))
]
self.board = []
self.n_players = n_players
self.bets = np.zeros(n_players) + 10
self.money = np.ones(n_players) * 90
self.ins = np.ones(n_players).astype(bool)
self.all_ins = np.zeros(n_players).astype(bool)
self.round = 0
def __init__(self, numPlayers, maxRaise, deck=False):
self.numPlayers = numPlayers
self.maxRaise = maxRaise + 1
if deck:
self.deck = deck
else:
self.deck = Deck()
self.boardLength = 5
def setUp(self) -> None:
self.players = [
Player(handle=f"p{n}", hand=Hand([])) for n in range(1, 5)
]
self.game = Game(little_ante=25,
big_ante=50,
players=self.players,
deck=Deck())
self.game_service = GameService(game=self.game)
def setup(n, m):
_hands = []
_boards = []
for i in range(n):
deck = Deck()
hand = []
board = []
for j in range(2):
hand.append(deck.draw())
for j in range(m):
board.append(deck.draw())
_hands.append(hand)
_boards.append(board)
return _boards, _hands
def generate(mdp, state, action):
cardsInUse = []
for player in state['players']:
hand = player[0]
if not hand:
continue
for card in hand:
cardsInUse.append(card)
for card in state['board']:
cardsInUse.append(card)
fullDeck = Deck().GetFullDeck()
deck = [card for card in fullDeck if card not in cardsInUse]
random.shuffle(deck)
#Update state based on action
rewards = mdp.getRewards(state, action)
if action == -1:
#Player folded, set their hand to False
state['players'][state['curPlayer']] = (
False, state['players'][state['curPlayer']][1])
else:
#Add players action to their running total bet
if (state['players'][state['curPlayer']][1] == False):
newPlayerBet = action #if current bet is False
else:
newPlayerBet = state['players'][state['curPlayer']][1] + action
state['players'][state['curPlayer']] = (
state['players'][state['curPlayer']][0], newPlayerBet)
#Total bet for the round is equal to the player's total running bet
state['curBet'] = newPlayerBet
#Add the players bet to the pot
state['pot'] += action
if mdp.roundIsOver(state):
if len(state['board']) == 0:
#Flop
newcards = [deck.pop(0) for _ in range(3)]
state['board'] = state['board'] + newcards
else:
#Turn or River or End of round
state['board'] = state['board'] + [deck.pop(0)]
#Reset current bet for the round
state['curBet'] = 0
#Rotate starting bet
state['curPlayer'] = mdp.getStartingPlayer(state)
# Reset player bets for all players
for i, player in enumerate(state['players']):
state['players'][i] = (state['players'][i][0], False)
else:
state['curPlayer'] += 1
state['curPlayer'] %= mdp.numPlayers
return state, rewards
def deal_cards(nb_players=2):
all_cards = [ Deck().draw(5 + 2*nb_players) for x in range(1000) ]
df = pd.DataFrame({'all': all_cards})
df['board'] = df['all'].apply(lambda x: x[:5])
for i in range(nb_players):
i_start = 5 + 2*i
i_end = i_start + 2
df['player{0}'.format(i+1)] = df['all'].apply(lambda x: list(islice(x, i_start, i_end)))
del df['all']
return df
def get_score_by_simulate(board_cards, hand_cards, iteration=5):
try:
score_min = 10000 # deuces evaluate score, the small, the better
board = []
for x in board_cards:
x = x.encode('ascii', 'ignore')
c = Card.new('{0}{1}'.format(x[0], x[1].lower()))
board.append(c)
hand = []
for x in hand_cards:
x = x.encode('ascii', 'ignore')
c = Card.new('{0}{1}'.format(x[0], x[1].lower()))
hand.append(c)
if (len(hand) + len(board) < 5):
score_list = []
for i in range(iteration):
evaluator = Evaluator()
deck = Deck()
random_cards = deck.draw(5 - len(board) - len(hand))
if (isinstance(random_cards, int)):
random_cards = [random_cards]
score = evaluator.evaluate(board + random_cards, hand)
score_list.append(score)
score_list.remove(max(score_list))
score_list.remove(min(score_list))
return sum(score_list) / float(len(score_list))
else:
for board_five_match in combinations(board, 5 - len(hand)):
evaluator = Evaluator()
score = evaluator.evaluate(tuple(board_five_match),
tuple(hand))
if (score < score_min):
score_min = score
return score_min
except Exception as e:
traceback.print_exc()
printtolog('EXCEPTION={0}'.format(e))
return score_min
def test_board(self):
random.seed(0)
hand = [Deck() for _ in range(5)]
df_board = pd.DataFrame({'board': [deck.draw(5) for deck in hand]})
expected = [[[81922, 33560861, 139523, 16783383, 147715]],
[[16783383, 4212241, 295429, 69634, 81922]],
[[266757, 1082379, 134236965, 557831, 16787479]],
[[33564957, 8423187, 1065995, 33589533, 98306]],
[[16795671, 529159, 135427, 16783383, 266757]]]
self.assertEqual(df_board.values.tolist(), expected)
def calc_hand_strength(self, game):
score = [0, 0]
for i in range(0, 1000):
deck = Deck()
player1_hand = self.hand
player2_hand = deck.draw(2)
if len(list(set(player1_hand + game.table).intersection(player2_hand))) == 0:
p1_score = game.evaluator.evaluate(game.table, player1_hand)
p2_score = game.evaluator.evaluate(game.table, player2_hand)
if p1_score < p2_score:
score[0] += 1
score[1] += 1
elif p2_score < p1_score:
score[1] += 1
else:
score[0] += 1
score[1] += 2
strength = score[0] / score[1]
return strength
def build_dataframe(nb_hands=1000):
decks = [Deck() for _ in range(nb_hands)]
boards = [deck.draw(5) for deck in decks]
player1 = [deck.draw(2) for deck in decks]
player2 = [deck.draw(2) for deck in decks]
e = Evaluator()
score1 = [e.evaluate(b, h) for (b, h) in zip(boards, player1)]
score2 = [e.evaluate(b, h) for (b, h) in zip(boards, player2)]
return pd.DataFrame({'score1': score1, 'score2': score2})
def main():
deck = Deck()
board = deck.draw(5)
solution = Solution()
currentPopulation = 0
#Generate initial population
#Add the new population to the solution
population = initial_population(NUMBER_OF_PLAYERS,deck)
init_pop = Population(population, currentPopulation+1)
solution.add_population(init_pop)
while (currentPopulation < 10):
# select chromosomes from the current population to apply cross_over and mutation
solution.populations[currentPopulation].select_random_chromosomes()
#Cross over and mutation
new_population = solution.populations[currentPopulation].cross_over()
currentPopulation += 1
pop = Population(new_population, currentPopulation+1)
solution.add_population(pop)
print (solution)