function [n_cand,Cand_models]=refine_kv(Model,InpPat)

n_cand=0;
Cand_models=Model;
n_s_b=Model.n_s_m;

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

[n_pat,n_inp]=size(InpPat);
add_comp=n_pat-comp;

% refine knot vector
for k=1:n_s_b
   this_comp=sum(Model.Sub_Model(k).a_w);
   Cur_Model=getfield(Model,'Sub_Model',{k});
	n_var=length(Cur_Model.var);

	for i=1:n_var
      pos=Cur_Model.order(i);
      this_one=pos+Cur_Model.int_knots(i);
      if this_comp/this_one*(this_one+1)<=add_comp
   		for j=1:(Cur_Model.int_knots(i)+1)
      		n_cand=n_cand+1;
      		Cand_models(n_cand)=Model;
      		n_knots=getfield(Cand_models(n_cand).Sub_Model(k),'n_Knots');
      		Knots=getfield(Cand_models(n_cand).Sub_Model(k),'Knots');
      
      		Knots=[Knots(i,1:pos) (Knots(i,pos)+Knots(i,pos+1))/2 Knots(i,(pos+1):n_knots(i))];
				Cand_models(n_cand).Sub_Model(k).Knots(i,1:n_knots(i)+1)=Knots;
      		Cand_models(n_cand).Sub_Model(k).n_Knots(i)=n_knots(i)+1;
      
      		Cand_models(n_cand).Sub_Model(k).n_fun(i)=Cand_models(n_cand).Sub_Model(k).n_fun(i)+1;
      
         	Cand_models(n_cand).Sub_Model(k).w=zeros(prod(Cand_models(n_cand).Sub_Model(k).n_fun),1);
         
         	Cand_models(n_cand).Sub_Model(k).a_w=ones(length(Cand_models(n_cand).Sub_Model(k).w),1);
      
      		Cand_models(n_cand).Sub_Model(k).int_knots(i)=Cand_models(n_cand).Sub_Model(k).int_knots(i)+1;

				pos=pos+1;
         end
      else
          disp(['This candidate -' num2str(k) ' - would generate ' num2str(comp+this_comp/this_one*(this_one+1)) ...
                ' complexity. Not enough training data!']);
      end    

   end   
end