def batching(size):
print('running batching')
xbat = []
ybat = []
b = Baraja()
for _ in range(size):
b.revolver()
xbat.append(b.one_hot())
ybat.append(b.approx_best_move(sample_size=150)[0])
return xbat, ybat
def test_one_hot_inv(self):
B = Baraja()
B.revolver()
bin_lst = B.one_hot()
self.assertEqual(B.sacar_mano(5), Mano(one_hot_inv(bin_lst)))
self.assertEqual(len(bin_lst), 52)
def test_one_hot(self):
B = Baraja()
bin_lst = B.one_hot()
self.assertEqual(sum(bin_lst), 5)
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)
