class Sep_test(object):
def __init__(self,feature_size):
'''
self.total_runs: the total times of making prediction
self.total_reward: the total reward of prediction
B, miu, f are median parameters
'''
self.linucb = LinUCB(feature_size)
self.lts = LTS(feature_size)
self.stat = Statistic(feature_size)
self.his_linucb = []
self.his_lts = []
self.his_stat = []
self.valid_linucb = 0
self.valid_lts = 0
self.valid_stat = 0
def LinUCB_predict_and_learn(self,context,articleID,reward,pool):
prediction = self.linucb.predict(context,pool)
#update records
if prediction==articleID:
self.his_linucb.append(reward)
self.valid_linucb += 1
#train one of the agents
self.linucb.learn(context,articleID,reward)
def lts_predict_and_learn(self,context,articleID,reward,pool):
prediction = self.lts.predict(context,pool)
#update records
if prediction==articleID:
self.his_lts.append(reward)
self.valid_lts += 1
#train one of the agents
self.lts.learn(context,articleID,reward)
def stat_predict_and_learn(self,context,articleID,reward,pool):
prediction = self.stat.predict(context,pool)
#update records
if prediction==articleID:
self.his_stat.append(reward)
self.valid_stat += 1
#train one of the agents
self.stat.learn(context,articleID,reward)
def predict_and_learn(self,context,articleID,reward,pool):
self.LinUCB_predict_and_learn(context,articleID,reward,pool)
self.lts_predict_and_learn(context,articleID,reward,pool)
self.stat_predict_and_learn(context,articleID,reward,pool)
class Sep_test(object):
def __init__(self, feature_size):
'''
self.total_runs: the total times of making prediction
self.total_reward: the total reward of prediction
B, miu, f are median parameters
'''
self.linucb = LinUCB(feature_size)
self.lts = LTS(feature_size)
self.stat = Statistic(feature_size)
self.his_linucb = []
self.his_lts = []
self.his_stat = []
self.his_hybrid = []
self.valid_linucb = 0
self.valid_lts = 0
self.valid_stat = 0
self.valid_hybrid = 0
self.vote = []
def LinUCB_predict_and_learn(self, context, articleID, reward, pool):
prediction = self.linucb.predict(context, pool)
self.vote.append(prediction)
#update records
if prediction == articleID:
self.his_linucb.append(reward)
self.valid_linucb += 1
#train one of the agents
self.linucb.learn(context, articleID, reward)
def lts_predict_and_learn(self, context, articleID, reward, pool):
prediction = self.lts.predict(context, pool)
self.vote.append(prediction)
#update records
if prediction == articleID:
self.his_lts.append(reward)
self.valid_lts += 1
#train one of the agents
self.lts.learn(context, articleID, reward)
def stat_predict_and_learn(self, context, articleID, reward, pool):
prediction = self.stat.predict(context, pool)
self.vote.append(prediction)
#update records
if prediction == articleID:
self.his_stat.append(reward)
self.valid_stat += 1
#train one of the agents
self.stat.learn(context, articleID, reward)
def predict_and_learn(self, context, articleID, reward, pool):
self.LinUCB_predict_and_learn(context, articleID, reward, pool)
self.lts_predict_and_learn(context, articleID, reward, pool)
self.stat_predict_and_learn(context, articleID, reward, pool)
def Hybrid_predict_and_learn(self, context, articleID, reward, pool):
self.predict_and_learn(context, articleID, reward, pool)
counts = np.bincount(self.vote)
prediction = choice(np.flatnonzero(counts == counts.max()))
#update records
if prediction == articleID:
self.his_hybrid.append(reward)
self.valid_hybrid += 1
self.vote = []
