noise = lambda : np.random.normal(scale = NoiseScale),
matrixNoise = lambda : np.random.normal(scale = matrixNoise),
batchSize = batchSize,
type_ = "UniformTheta",
signature = AM.signature,
sparseLevel = sparseLevel,
poolArticleSize = poolSize, NoiseScale = NoiseScale, epsilon = epsilon, Gepsilon =Gepsilon)
print "Starting for ", simExperiment.simulation_signature
for i in range(10):
algorithms = {}
#algorithms['LinUCB'] = N_LinUCBAlgorithm(dimension = dimension, alpha = alpha, lambda_ = lambda_, n = n_users)
#algorithms['GOBLin'] = GOBLinAlgorithm( dimension= dimension, alpha = G_alpha, lambda_ = G_lambda_, n = n_users, W = simExperiment.getGW() )
#algorithms['syncCoLinUCB'] = syncCoLinUCBAlgorithm(dimension=dimension, alpha = alpha, lambda_ = lambda_, n = n_users, W = simExperiment.getW())
#algorithms['AsyncCoLinUCB_100'] = AsyCoLinUCBAlgorithm(dimension=dimension, alpha = alpha, lambda_ = lambda_, n = n_users, W = simExperiment.getW())
#algorithms['UniformLinUCB'] = Uniform_LinUCBAlgorithm(dimension = dimension, alpha = alpha, lambda_ = lambda_)
#algorithms['WCoLinUCB'] = WAlgorithm(dimension = dimension, alpha = alpha, lambda_ = lambda_, eta_ = eta_, n = n_users)
#algorithms['WknowTheta'] = WknowThetaAlgorithm(dimension = dimension, alpha = alpha, lambda_ = lambda_, eta_ = eta_, n = n_users, theta = simExperiment.getTheta())
#algorithms['W_W0'] = W_W0_Algorithm(dimension = dimension, alpha = alpha, lambda_ = lambda_, eta_ = eta_, n = n_users, W0 = simExperiment.getW0())
#algorithms['eGreedy'] = eGreedyAlgorithm(epsilon = eGreedy)
#algorithms['UCB1'] = UCB1Algorithm()
userReorderAlgorithms = {}
userReorderAlgorithms['CoLinReorder_var_noise0.1_100/10'] = AsyCoLinUCBAlgorithm(dimension=dimension, alpha = alpha, lambda_ = lambda_, n = n_users, W = simExperiment.getW())
#userReorderAlgorithms['LinUCBReorder'] = N_LinUCBAlgorithm(dimension=dimension, alpha = alpha, lambda_ = lambda_, n = n_users)
simExperiment.runAlgorithms(algorithms)
fileNameWriteCluster = os.path.join(Kmeansdata_address, '10kmeans_model160.dat.'+str(clusterNum)+ '.max_observation')
userFeatureVectors = getClusters(fileNameWriteCluster)
userNum = clusterNum
if DiagType == 'Orgin':
W = initializeW(userFeatureVectors, SparsityLevel)
elif DiagType == 'Opt':
W = initializeW_opt(userFeatureVectors, SparsityLevel) # Generate user relation matrix
GW = initializeGW(W , epsilon)
articles_random = randomStruct()
algorithms = {}
runCoLinUCB = runGOBLin = runLinUCB = run_M_LinUCB = run_Uniform_LinUCB= run_CFUCB = run_CFEgreedy = run_SGDEgreedy = run_PTS = run_TCF = False
if args.alg:
if args.alg == 'CoLinUCB' or args.alg == 'CoLin':
runCoLinUCB = True
algorithms['CoLin'] = AsyCoLinUCBAlgorithm(dimension=context_dimension, alpha = alpha, lambda_ = lambda_, n = userNum, W = W)
elif args.alg == 'GOBLin':
runGOBLin = True
algorithms['GOBLin'] = GOBLinAlgorithm(dimension=context_dimension, alpha = alpha, lambda_ = lambda_, n = userNum, W = GW)
elif args.alg == 'LinUCB':
runLinUCB = True
algorithms['LinUCB'] = N_LinUCBAlgorithm(dimension = context_dimension, alpha = alpha, lambda_ = lambda_, n = clusterNum)
elif args.alg =='M_LinUCB':
run_M_LinUCB = True
elif args.alg == 'Uniform_LinUCB':
run_Uniform_LinUCB = True
elif args.alg == 'factorLinUCB':
run_factorLinUCB = True
if not args.dimension:
dimension = 5
else:
# algorithms['CFEgreedy'] = CFEgreedyAlgorithm(context_dimension = context_dimension, latent_dimension = latent_dimension, alpha = alpha, lambda_ = lambda_, n = n_users, itemNum=n_articles, init='random')
# algorithms['UCBPMF0.1'] = UCBPMFAlgorithm(dimension = 10, n = n_users, itemNum=n_articles, sigma = np.sqrt(.5), sigmaU = 1, sigmaV = 1, alpha = 0.1)
algorithms['CFUCB'] = CFUCBAlgorithm(context_dimension=context_dimension,
latent_dimension=5,
alpha=0.1,
alpha2=0.1,
lambda_=lambda_,
n=n_users,
itemNum=n_articles,
init='zero',
window_size=-1)
algorithms['AsyncCoLinUCB'] = AsyCoLinUCBAlgorithm(
dimension=context_dimension,
alpha=alpha,
lambda_=lambda_,
n=n_users,
W=simExperiment.getW())
algorithms['FactorLinUCBAlgorithm'] = FactorLinUCBAlgorithm(
context_dimension=context_dimension,
latent_dimension=5,
alpha=0.1,
alpha2=0.1,
lambda_=lambda_,
n=n_users,
itemNum=n_articles,
W=simExperiment.getW(),
init='zero',
window_size=-1)