objfun.m

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Function:         S_MSE= objfun(FVr_temp, S_struct)
% Author:           Rainer Storn
% Description:      Implements the cost function to be minimized.
% Parameters:       FVr_temp     (I)    Paramter vector
%                   S_Struct     (I)    Contains a variety of parameters.
%                                       For details see Rundeopt.m
% Return value:     S_MSE.I_nc   (O)    Number of constraints
%                   S_MSE.FVr_ca (O)    Constraint values. 0 means the constraints
%                                       are met. Values > 0 measure the distance
%                                       to a particular constraint.
%                   S_MSE.I_no   (O)    Number of objectives.
%                   S_MSE.FVr_oa (O)    Objective function values.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function S_MSE= objfun(FVr_temp, S_struct)
load reg
y = size(regressorX);
% y(2) is the number of features
theta=zeros(y(2) ,1);

for i=1:y(2)
    theta(i)=FVr_temp(i);
end
theta0=FVr_temp(y(2)+1);
F_cost=norm((regressorY' - (regressorX*theta + theta0)));


% 
% %---Peaks function----------------------------------------------
% F_cost = peaks(FVr_temp(1),FVr_temp(2));

%----strategy to put everything into a cost function------------
S_MSE.I_nc      = 0;%no constraints
S_MSE.FVr_ca    = 0;%no constraint array
S_MSE.I_no      = 1;%number of objectives (costs)
S_MSE.FVr_oa(1) = F_cost;
end