ccl_obj_AUn

 [fun] = ccl_obj_AUn (model, W, BX, RnUn)
 Objective funtion: minimise (A * Un)^2

 Input:
   model                                Model related parameteres
   W                                    Weights of parametric model
   BX                                   Higher dimensional representation of X using gaussian kernel
   RnUn                                 RnUn=Rn*Un
 Output:
   fun                                  Returned objective function handle

0001 function [fun] = ccl_obj_AUn (model, W, BX, RnUn)
0002 % [fun] = ccl_obj_AUn (model, W, BX, RnUn)
0003 % Objective funtion: minimise (A * Un)^2
0004 %
0005 % Input:
0006 %   model                                Model related parameteres
0007 %   W                                    Weights of parametric model
0008 %   BX                                   Higher dimensional representation of X using gaussian kernel
0009 %   RnUn                                 RnUn=Rn*Un
0010 % Output:
0011 %   fun                                  Returned objective function handle
0012 
0013 dim_n   = size(BX,2) ;
0014 W       = reshape(W, model.dim_u-model.dim_k, model.dim_b );
0015 fun     = zeros(dim_n, 1) ;
0016 theta   = [pi/2*ones(dim_n, (model.dim_k-1)), (W * BX)' ] ;
0017 alpha   = ccl_math_uvec(theta) ;
0018 for n   = 1 : dim_n
0019     fun(n)  = alpha(n,:) * RnUn(:,n) ;
0020 end
0021 end