diff --git a/Simulation.py b/Simulation.py
index 23c2cc1..a80e711 100644
--- a/Simulation.py
+++ b/Simulation.py
@@ -293,7 +293,7 @@ def runAlgorithms(self, algorithms):
 
 					#update parameter estimation record
 					if alg.CanEstimateUserPreference:
-						ThetaDiff[alg_name] += self.getL2Diff(u.theta, alg.getTheta(u.id))
+						ThetaDiff[alg_name] += self.getL2Diff(u.theta[:self.context_dimension], alg.getTheta(u.id))
 					if alg.CanEstimateCoUserPreference:
 						CoThetaDiff[alg_name] += self.getL2Diff(u.CoTheta[:self.context_dimension], alg.getCoTheta(u.id)[:self.context_dimension])
 					if alg.CanEstimateW:
@@ -504,7 +504,7 @@ def pca_articles(articles, order):
 	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 args.alg == 'factorUCB':
-		algorithms['FactorUCB'] = FactorUCBAlgorithm(context_dimension = context_dimension, latent_dimension = 5, alpha = 0.05, alpha2 = 0.025, lambda_ = lambda_, n = n_users, itemNum=n_articles, W = simExperiment.getW(), init='random', window_size = -1)	
+		algorithms['FactorUCB'] = FactorUCBAlgorithm(context_dimension = context_dimension, latent_dimension = latent_dimension, alpha = 0.05, alpha2 = 0.025, lambda_ = lambda_, n = n_users, itemNum=n_articles, W = simExperiment.getW(), init='random', window_size = -1)	
 		algorithms['LinUCB'] = N_LinUCBAlgorithm(dimension = context_dimension, alpha = alpha, lambda_ = lambda_, )
 	if args.alg == 'CoLin':
 		algorithms['CoLin'] = AsyCoLinUCBAlgorithm(dimension=context_dimension, alpha = alpha, lambda_ = lambda_, n = n_users, W = simExperiment.getW())
@@ -514,11 +514,10 @@ def pca_articles(articles, order):
 		algorithms['CLUB'] = CLUBAlgorithm(dimension =context_dimension,alpha = alpha, lambda_ = lambda_, n = n_users, alpha_2 = 0.5, cluster_init = 'Erdos-Renyi')	
 	if algName == 'All':
 		algorithms['LinUCB'] = N_LinUCBAlgorithm(dimension = context_dimension, alpha = alpha, lambda_ = lambda_)
-		algorithms['hLinUCB'] = HLinUCBAlgorithm(context_dimension = context_dimension, latent_dimension = 5, alpha = 0.1, alpha2 = 0.1, lambda_ = lambda_, itemNum=n_articles, init='random', window_size = -1)	
+		algorithms['hLinUCB'] = HLinUCBAlgorithm(context_dimension = context_dimension, latent_dimension = latent_dimension, alpha = 0.1, alpha2 = 0.1, lambda_ = lambda_, n = n_users, itemNum=n_articles, init='random', window_size = -1)	
 		algorithms['PTS'] = PTSAlgorithm(particle_num = 10, dimension = 10, n = n_users, itemNum=n_articles, sigma = np.sqrt(.5), sigmaU = 1, sigmaV = 1)
-		algorithms['HybridLinUCB'] = Hybrid_LinUCBAlgorithm(dimension = context_dimension, alpha = alpha, lambda_ = lambda_, userFeatureList=simExperiment.generateUserFeature(simExperiment.getW()))
 		algorithms['UCBPMF'] = UCBPMFAlgorithm(dimension = 10, n = n_users, itemNum=n_articles, sigma = np.sqrt(.5), sigmaU = 1, sigmaV = 1, alpha = 0.1) 
 		algorithms['CoLin'] = AsyCoLinUCBAlgorithm(dimension=context_dimension, alpha = alpha, lambda_ = lambda_, n = n_users, W = simExperiment.getW())
-		algorithms['factorUCB'] = FactorUCBAlgorithm(context_dimension = context_dimension, latent_dimension = 5, alpha = 0.05, alpha2 = 0.025, lambda_ = lambda_, n = n_users, itemNum=n_articles, W = simExperiment.getW(), init='zero', window_size = -1)	
+		algorithms['factorUCB'] = FactorUCBAlgorithm(context_dimension = context_dimension, latent_dimension = latent_dimension, alpha = 0.05, alpha2 = 0.025, lambda_ = lambda_, n = n_users, itemNum=n_articles, W = simExperiment.getW(), init='zero', window_size = -1)	
 
 	simExperiment.runAlgorithms(algorithms)
\ No newline at end of file