Minor modifications to admm

@admm/admm.m

@@ -2,19 +2,19 @@
 
 	methods(Static)
 
-		output 	= admm_solver(Sigma,varargin); 
+		output 			= admm_solver(Sigma,varargin); 
 
-		options = admm_options(varargin);		
+		options 		= admm_options(varargin);		
 
-		options = initializeXZU(Sigma,varargin); 
+		options 		= initializeXZU(Sigma,varargin); 
 
-		output  = objective(Sigma,Theta,Z,Lambda);
+		output  		= objective(Sigma,Theta,Z,Lambda);
 
-		[X,Z] = update_X(Sigma,X,Z,U,options); 	
+		[X,Z] 			= update_X(Sigma,X,Z,U,options); 	
 
-		[Z_new,X_new] = update_Z(X,Z,U,options); 					
+		[Z_new,X_new] 	= update_Z(X,Z,U,options); 					
 
-		U = update_U(X,Z,U); 
+		U 				= update_U(X,Z,U); 
 
 	end
 

test/test_GGM.m

@@ -1,5 +1,5 @@
 function test_suite = test_GGM
-	initTestSuite;
+	buildFunctionHandleTestSuite(localfunctions);
 
 end
 
@@ -33,16 +33,18 @@
 	clear GGMobj;
 	GGMobj = GGM(Data,1,0);
 	GGMobj.sparseMLE();
+
 
-	if(GGMobj.verbose)
-		gr_sparsity = squeeze(sum(sum(GGMobj.ThetaPath~=0,1),2))'; 
-		gr_sparsity(1:10)
-		gr_sparsity = squeeze(sum(sum(GGMobj.AdaptTheta~=0,1),2))';
-		gr_sparsity(1:10)
-	end
-
-	% Check plotting of regularization path
-	GGMobj.plot();
+	% UNTESTED
+	% if(GGMobj.verbose)
+	% 	gr_sparsity = squeeze(sum(sum(GGMobj.ThetaPath~=0,1),2))';
+	% 	n_lambdas = min(25,floor(.25*numel(gr_sparsity)));
+	% 	gr_sparsity(1:n_lambdas)
+	% 	gr_sparsity = squeeze(sum(sum(GGMobj.AdaptTheta~=0,1),2))';
+	% 	gr_sparsity(1:n_lambdas)
+	% end
+	% % Check plotting of regularization path
+	% GGMobj.plot();
 
 	% Check Model Selection
 	currstate = rng;
@@ -56,7 +58,12 @@
 	% Check tylerMLE
 	GGMobj = GGM(Data);
 	[GGMobj results] = GGMobj.tylerMLE();
-
+
+	% Check debias
+	GGMobj = GGM(Data); 
+	GGMobj.debias();
+
+	clear n_lambda;	
 end
 
 % 	% Initialize Variables