Delete helpers.py

helpers.py

@@ -1,49 +0,0 @@
-from mabwiser.mab import MAB, LearningPolicy
-
-def bandit_factory(bandit_type, arms):
-  if bandit_type == "Epsilon Greedy":
-    result = MAB(arms=arms,
-             learning_policy=LearningPolicy.EpsilonGreedy(epsilon=0.3),
-             seed=1234)
-  elif bandit_type == "UCB":
-    result = MAB(arms=arms,
-             learning_policy=LearningPolicy.UCB1(alpha=1),
-             seed=1234)
-  elif bandit_type == "Non-Stationary":
-    result = NSBandit(arms=arms, epsilon=0.3, alpha=0.2)
-  else:
-    raise ValueError("Invalid bandit type")
-
-  result.partial_fit(decisions=arms, rewards=[3]*len(arms))
-  return result
-
-class NSBandit:
-  def __init__(self, arms, epsilon, alpha):
-    self.arms = arms
-    self.epsilon = epsilon
-    self.alpha = alpha
-    self.means = {arm: None for arm in arms}
-    self.t = 0
-
-  def partial_fit(self, decisions, rewards):
-    for arm, reward in zip(decisions, rewards):
-      if self.means[arm] is None:
-        self.means[arm] = reward
-      else:
-        self.means[arm] += self.alpha * (reward - self.means[arm])
-    self.t += 1
-
-
-  def predict(self):
-    nones = [t[0] for t in self.means.items() if t[1] is None]
-    if len(nones) > 0:
-      return random.choice(nones)
-
-    best = max(self.means, key=self.means.get)
-
-    if random.random() < self.epsilon:
-      return random.choice(list(set(self.arms) - {best}))
-    else:
-      return max(self.means, key=self.means.get)
-
-