def thompson_sampling(T: int, mu, seed=None, prior_SF=None, **_kwargs):
""" Bernoulli Thompson Sampling with known mu"""
K_ = len(mu)
S, F, theta = np.zeros((K_, )), np.zeros((K_, )), np.zeros((K_, ))
if prior_SF is not None:
S, F = prior_SF
arms_selected = np.zeros((T, )).astype(int)
rewards = np.zeros((T, ))
with seeded(seed):
random_numbers = np.random.rand(T)
for t in range(T):
theta = [beta(S[i] + 1, F[i] + 1) for i in range(K_)]
arm_x = rand_argmax(theta)
reward_y = int(random_numbers[t] <= mu[arm_x])
arms_selected[t] = arm_x
rewards[t] = reward_y
if reward_y == 1:
S[arm_x] += 1
else:
F[arm_x] += 1
return arms_selected, rewards
def classify(self, instance):
(label, p1, p2) = instance
useLabel = label
if bernoulli(self.epsilon):
useLabel = not label
if self.simple:
res = float(useLabel)
else:
if useLabel:
res = beta(self.aIn, self.bIn)
else:
res = beta(self.aOut, self.bOut)
self.scores.add(label, int(res > .5))
return res
def beta(a, b, shape=[]):
"""beta(a, b) or beta(a, b, [n, m, ...]) returns array of beta distributed random numbers."""
if shape == []:
shape = None
return mt.beta(a, b, shape)
def beta(a, b, shape=[]):
"""beta(a, b) or beta(a, b, [n, m, ...]) returns array of beta distributed random numbers."""
if shape == []:
shape = None
return mt.beta(a, b, shape)
def test_beta_small_parameters(self):
# Test that beta with small a and b parameters does not produce
# NaNs due to roundoff errors causing 0 / 0, gh-5851
random.seed(1234567890)
x = random.beta(0.0001, 0.0001, size=100)
assert_(not np.any(np.isnan(x)), 'Nans in random.beta')