function [n_cand,Cand_models]=add_uni_sm(Model,InpPat,order);

n_cand=0;
n_s_b=Model.n_s_m;
[n_pat,n_var]=size(InpPat);

%determine current complexity
comp=0;
for i=1:n_s_b
   comp=comp+sum(Model.Sub_Model(i).a_w);
end

add_comp=n_pat-comp;

% determine if it is going to add another univariate sub-model

pre_var=zeros(1,n_var);
for i=1:n_s_b
   n_var_i=Model.Sub_Model(i).var;
   for j=1:length(n_var_i)
      pre_var(n_var_i(j))=1;  
   end
end

add_var=ones(1,n_var)-pre_var;

for i=1:n_var
   if add_var(i)==1
      if order<=add_comp
   		% add variable
      	x_min=min(InpPat);
      	x_max=max(InpPat)+10*eps*ones(1,n_var);
      	[Knots,n_Knots,n_fun,w,a_w]=create_B_spline(x_min(i),x_max(i),0,order);
     		n_cand=n_cand+1;
 			Cand_models(n_cand)=Model;
			n_s_b1=n_s_b+1;
      	Cand_models(n_cand).n_s_m=n_s_b+1;
      	Cand_models(n_cand).Sub_Model(n_s_b1).Knots=Knots;
      	Cand_models(n_cand).Sub_Model(n_s_b1).n_Knots=n_Knots;
      	Cand_models(n_cand).Sub_Model(n_s_b1).n_fun=n_fun;
      	Cand_models(n_cand).Sub_Model(n_s_b1).w=w;
      	Cand_models(n_cand).Sub_Model(n_s_b1).a_w=a_w;   
      	Cand_models(n_cand).Sub_Model(n_s_b1).x_min=x_min(i);
			Cand_models(n_cand).Sub_Model(n_s_b1).x_max=x_max(i);
      	Cand_models(n_cand).Sub_Model(n_s_b1).order=order;
      	Cand_models(n_cand).Sub_Model(n_s_b1).int_knots=0;
      	Cand_models(n_cand).Sub_Model(n_s_b1).var=i;
      else
         disp(['This candidate -' num2str(new_var) ' - would generate ' num2str(comp+new_comp) ...
                  ' complexity. Not enough training data!']);
      end   
   end    
end   

if n_cand==0
   Cand_models=[];
end