Fixed indentation.

src/auxiliary_initialization.m

@@ -87,7 +87,7 @@ yhat = bsxfun(@minus,StateVectors,state_variables_steady_state);
 %    yhat_ = bsxfun(@minus,StateVectors_,state_variables_steady_state);
 %    [tmp, tmp_] = local_state_space_iteration_2(yhat,zeros(number_of_structural_innovations,number_of_particles),ghx,ghu,constant,ghxx,ghuu,ghxu,yhat_,steadystate,ThreadsOptions.local_state_space_iteration_2);
 %else
-    tmp = local_state_space_iteration_2(yhat,zeros(number_of_structural_innovations,number_of_particles),ghx,ghu,constant,ghxx,ghuu,ghxu,ThreadsOptions.local_state_space_iteration_2);
+tmp = local_state_space_iteration_2(yhat,zeros(number_of_structural_innovations,number_of_particles),ghx,ghu,constant,ghxx,ghuu,ghxu,ThreadsOptions.local_state_space_iteration_2);
 %end
 PredictedObservedMean = weights*(tmp(mf1,:)');
 PredictionError = bsxfun(@minus,Y(:,t),tmp(mf1,:));

src/fit_gaussian_mixture.m

@@ -49,4 +49,3 @@ for n=1:niters
         end
     end
 end
-

src/gaussian_mixture_densities.m

@@ -54,4 +54,3 @@ sqr_det = sqrt(det(Pyy)) ;
 foo = (eta_t_i/Pyy).*eta_t_i ;
 likelihood = exp(-0.5*sum(foo,2))/(normconst*sqr_det) + 1e-99 ;
 IncrementalWeights = likelihood.*prior./proposal ;
-

src/importance_sampling.m

@@ -26,4 +26,3 @@ for k=1:Gsecond
     State_Particles(:,idx) = StateSqrtPPost(:,:,k)*randn(Xdim,Nc(k));
 end
 State_Particles= State_Particles + StateMuPost(:,comp);
-    

src/measurement_equations.m

@@ -28,6 +28,3 @@ state_variables_steady_state = ReducedForm.state_variables_steady_state;
 number_of_structural_innovations = length(ReducedForm.Q);
 yhat = bsxfun(@minus,StateVectors,state_variables_steady_state) ;
 measure = local_state_space_iteration_2(yhat,zeros(number_of_structural_innovations,size(yhat,2)),ghx,ghu,constant,ghxx,ghuu,ghxu,ThreadsOptions.local_state_space_iteration_2);
-
-
-

src/online_auxiliary_filter.m

@@ -367,4 +367,3 @@ for plt = 1:nbplt,
         fprintf(fidTeX,' \n');
     end
 end
-    

src/solve_model_for_online_filter.m

@@ -185,18 +185,18 @@ if EstimatedParameters.ncx
         Q(k2,k1) = Q(k1,k2);
     end
     % Try to compute the cholesky decomposition of Q (possible iff Q is positive definite)
-%    [CholQ,testQ] = chol(Q);
-%    if testQ
+    %    [CholQ,testQ] = chol(Q);
+    %    if testQ
     % The variance-covariance matrix of the structural innovations is not definite positive. We have to compute the eigenvalues of this matrix in order to build the endogenous penalty.
-%        a = diag(eig(Q));
-%        k = find(a < 0);
-%        if k > 0
-%            fval = objective_function_penalty_base+sum(-a(k));
-%            exit_flag = 0;
-%            info = 43;
-%            return
-%        end
-%    end
+    %        a = diag(eig(Q));
+    %        k = find(a < 0);
+    %        if k > 0
+    %            fval = objective_function_penalty_base+sum(-a(k));
+    %            exit_flag = 0;
+    %            info = 43;
+    %            return
+    %        end
+    %    end
     offset = offset+EstimatedParameters.ncx;
 end
 
@@ -210,18 +210,18 @@ if EstimatedParameters.ncn
         H(k2,k1) = H(k1,k2);
     end
     % Try to compute the cholesky decomposition of H (possible iff H is positive definite)
-%    [CholH,testH] = chol(H);
-%    if testH
+    %    [CholH,testH] = chol(H);
+    %    if testH
     % The variance-covariance matrix of the measurement errors is not definite positive. We have to compute the eigenvalues of this matrix in order to build the endogenous penalty.
-%        a = diag(eig(H));
-%        k = find(a < 0);
-%        if k > 0
-%            fval = objective_function_penalty_base+sum(-a(k));
-%            exit_flag = 0;
-%            info = 44;
-%            return
-%        end
-%    end
+    %        a = diag(eig(H));
+    %        k = find(a < 0);
+    %        if k > 0
+    %            fval = objective_function_penalty_base+sum(-a(k));
+    %            exit_flag = 0;
+    %            info = 44;
+    %            return
+    %        end
+    %    end
     offset = offset+EstimatedParameters.ncn;
 end
 

src/unscented_sigma_points.m

@@ -38,5 +38,3 @@ W_c = W_m + (1-ParticleOptions.unscented.alpha^2+ParticleOptions.unscented.beta)
 temp = ones(2*n,1)/(2*(n+lambda)) ;
 W_m = [W_m ; temp] ;
 W_c = [W_c ; temp]  ;
-
-