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import numpy as np | |
class ContextualThompson(object): | |
def __init__(self, d=10, R=0.01, epsilon=0.5, delta=1.0, n_arms=10): | |
self.n_arms = n_arms | |
self.d = d | |
self.R = R | |
self.delta = delta | |
self.epsilon = epsilon | |
self.t = 0 | |
self.mu_hat = [np.zeros((self.d, 1)) for arm in range(n_arms)] | |
self.f = [np.zeros((self.d, 1)) for arm in range(n_arms)] | |
self.B = [np.identity(self.d) for arm in range(n_arms)] | |
def get_action(self, context): | |
self.t += 1 | |
v = self.R * np.sqrt(9 / self.epsilon * self.d * np.log(self.t / self.delta)) | |
scores = [] | |
for arm in range(self.n_arms): | |
mu_tilde = np.random.multivariate_normal(self.mu_hat[arm].flat, v**2 * np.linalg.inv(self.B[arm])) | |
scores.append(np.array(context).dot(mu_tilde)) | |
return np.argmax(scores) | |
def reward(self, context, action, reward): | |
cn = np.array([context]).T | |
self.B[action] += cn.dot(cn.T) | |
self.f[action] += reward * cn | |
self.mu_hat[action] = np.linalg.inv(self.B[action]).dot(self.f[action]) |
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