def test_play(self):
B = Baraja()
B.start_with(list(map(Naipe, ['2S', '2D', '3S', '2H', '2C'])))
self.assertEqual(Prize.Poker, B.play(28))
B.start_with(list(map(Naipe, ['2S', '2D', '3S', '3H', '2C'])))
self.assertEqual(Prize.FullHouse, B.play(31))
B = Baraja()
B.start_with(map(Naipe, ['4S', '2C', '8S', 'QC', '4D']))
self.assertEqual(Prize.TwoPair, B.play(18, rand_sampling=False))
def play_game(sample_size):
bb = Baraja()
bb.revolver()
A = bb.approx_best_move(sample_size=sample_size)
prize = bb.play(A[0]).value
logging.debug('%s -- %s -- %s' % (bb.preview(), act.actions[A[0]], prize))
return prize
import sys
from scipy.stats import chi2
# %load_ext autoreload
# %autoreload 2
import actions as act
from collections import Counter
from baraja import Naipe, Mano, Baraja, Prize
# #%run ~/code/EfedEquis/Efedequis/poquer/baraja/baraja
# %%time
b = Baraja()
b.start_with(map(Naipe,['6D', 'QH', '7H', '7S', '10C']))
#b.evaluate(13, sample_size=5000000)
prize_counter = Counter()
for _ in range(1000):
prize_counter.update([b.play(13)])
print(prize_counter.most_common())
b.approx_best_move(sample_size=300)
# %%time
b = Baraja()
b.start_with(map(Naipe,['3S', 'QS', 'JD', '7S', '6S']))
res = []
for i in range(32):
res.append((act.actions[i], b.evaluate_eff(i, sample_size=10)))
{k: v for k, v in sorted(res, key=lambda item: -item[1])}
best = b.approx_best_move(sample_size=100)
act.actions[best[0]],best[1]
# +
def evaluate_strategy(s, b, **kwargs):
'''
kwargs goes directly to the strategy function
'''
best = s(b, **kwargs)
print(act.actions[best])
return b.play(best, rand_sampling=False).value
print(b)
evaluate_strategy(strategy, b, sample_size=1000)
# -
B = Baraja()
B.start_with(map(Naipe, ['4S', '2C', '8S', 'QC', '4D']))
print(B)
B.play(18, rand_sampling=False)
# + jupyter={"outputs_hidden": true}
# %%time
b = Baraja()
b.start_with(map(Naipe,['AS', 'QS', 'QD', 'JS', '10S']))
res = []
for i in range(32):
res.append((act.actions[i], b.evaluate(i, sample_size=100)))
{k: v for k, v in sorted(res, key=lambda item: -item[1])}
# -
[i for i in range(5) if i not in act.actions[29] ], act.actions[29]
for n in iter(m):
print(Naipe(n).repr_naipe())
b.revolver()
xbat.append(b.one_hot())
ybat.append(b.approx_best_move(sample_size=150)[0])
return xbat, ybat
for _ in range(5):
print('started batching ')
pool = mp.Pool(processes=4)
ret = pool.map(batching, 4 * [10])
pool.close()
lx = reduce(lambda a, b: a + b, [r[0] for r in ret])
ly = reduce(lambda a, b: a + b, [r[1] for r in ret])
x_train = np.reshape(np.array(lx).astype(np.float32), [len(lx), 52])
y_train = np.reshape(np.array(ly).astype(np.float32), [len(ly), 1])
model.fit(x_train, y_train, epochs=5)
bar = Baraja()
num_games = 10
credit = 0
for _ in range(num_games):
bar.revolver()
oh = np.array(bar.one_hot(), ndmin=2)
acc = np.argmax(model.call(oh))
#acc = random.randint(0,31)
credit += bar.play(acc).value
print("The expected return is: ", credit / num_games - 1)
model_filepath = 'poker.mdl'
print('saving model at: ', model_filepath)
model.save(model_filepath)
