if not args.particle_num:
particle_num = 10
else:
particle_num = int(args.particle_num)
if not args.dimension:
dimension = 5
else:
dimension = int(args.dimension)
algorithms['PTS'] = PTSAlgorithm(particle_num = particle_num, dimension = dimension, n = clusterNum, itemNum=itemNum, sigma = np.sqrt(.5), sigmaU = 1, sigmaV = 1)
elif args.alg == 'UCBPMF':
run_UCBPMF = True
if not args.dimension:
dimension = 5
else:
dimension = int(args.dimension)
algorithms['UCBPMF'] = UCBPMFAlgorithm(dimension = dimension, n = clusterNum, itemNum=itemNum, sigma = np.sqrt(.5), sigmaU = 1, sigmaV = 1, alpha = 0.1)
elif args.alg == 'Hybrid_LinUCB':
algorithms['HybridLinUCB'] = Hybrid_LinUCBAlgorithm(dimension = context_dimension, alpha = alpha, lambda_ = lambda_, userFeatureList=userFeatureVectors)
elif args.alg == 'ALL':
runCoLinUCB = runGOBLin = runLinUCB = run_M_LinUCB = run_Uniform_LinUCB=True
else:
args.alg = 'Random'
for alg_name, alg in algorithms.items():
alg.learn_stats = articleAccess()
train_days = 1
#Train
if (not args.pretrain or args.pretrain == True):
# algorithms['CFUCB10'] = CFUCBAlgorithm(context_dimension = context_dimension, latent_dimension = 10, alpha = alpha, alpha2 = alpha, lambda_ = lambda_, n = n_users, itemNum=n_articles, init='random')
# algorithms['CFEgreedy10'] = CFEgreedyAlgorithm(context_dimension = context_dimension, latent_dimension = 10, alpha = alpha, lambda_ = lambda_, n = n_users, itemNum=n_articles, init='random', epsilon_init=200)
# algorithms['CFUCB2'] = CFUCBAlgorithm(context_dimension = context_dimension, latent_dimension = 2, alpha = alpha, alpha2 = alpha, lambda_ = lambda_, n = n_users, itemNum=n_articles, init='random')
# algorithms['CFEgreedy2'] = CFEgreedyAlgorithm(context_dimension = context_dimension, latent_dimension = 2, alpha = alpha, lambda_ = lambda_, n = n_users, itemNum=n_articles, init='random', epsilon_init=200)
if algName == 'HybridLinUCB':
algorithms['HybridLinUCB'] = Hybrid_LinUCBAlgorithm(
dimension=context_dimension,
alpha=alpha,
lambda_=lambda_,
userFeatureList=simExperiment.generateUserFeature(
simExperiment.getW()))
if args.alg == 'UCBPMF':
algorithms['UCBPMF'] = UCBPMFAlgorithm(dimension=10,
n=n_users,
itemNum=n_articles,
sigma=np.sqrt(.5),
sigmaU=1,
sigmaV=1,
alpha=0.1)
if algName == 'All':
algorithms['LinUCB'] = N_LinUCBAlgorithm(dimension=context_dimension,
alpha=alpha,
lambda_=lambda_,
n=n_users)
algorithms['CFUCB'] = CFUCBAlgorithm(
context_dimension=context_dimension,
latent_dimension=5,
alpha=0.1,
alpha2=0.1,
lambda_=lambda_,
n=n_users,