step11.asv

模式识别中的isodata算法

% step11.m (combination)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% construct a distance matrix
% it's a triangle matrix to avoid repeated distance
% each element notes the distance between two centers
% step11.m include ISODATA Step 11,12,13
ctr_dist=zeros(c,c); % center distance matrix
for m=1:c
   for n=1:c
      if m<n
         ctr_dist(m,n)=ecld_dist(Z(m,:),Z(n,:));
      else
         ctr_dist(m,n)=inf;
      end
   end
end
% ------------------------------------
% choose the classes that need to be combined
% Min_Dist_Ctr is the output of this part
% it contains the position of the smallest L centers
Min_Dist_Ctr=zeros(L,2);
for k=1:L
   [temp mincetrpos]=min(ctr_dist(:));%minimum center distance
   if temp<theta_c
      clear temp
      [Min_Dist_Ctr(k,1) Min_Dist_Ctr(k,2)]=ind2sub(size(ctr_dist),mincetrpos);
      ctr_dist(mincetrpos)=inf;
   end
end
%合过的不合
if sum(Min_Dist_Ctr(:))~=0
   for q=1:length(Min_Dist_Ctr(:))
      for p=q+1:length(Min_Dist_Ctr(:))
         if Min_Dist_Ctr(p)==Min_Dist_Ctr(q)
            [tempx tempy]=ind2sub(size(Min_Dist_Ctr), p);
            Min_Dist_Ctr(tempx,:)=[0 0];         
         end
      end
   end
end
% ------------------------------------
% combination according to Min_Dist_Ctr

if sum(Min_Dist_Ctr(:))~=0
   for k=1:size(Min_Dist_Ctr,1)
      if Min_Dist_Ctr(k,1)~=0
         sumtwoclass=s{5,Min_Dist_Ctr(k,1)}+s{5,Min_Dist_Ctr(k,2)};
         % add a new class center
         Z(end+1,:)= (s{5,Min_Dist_Ctr(k,1)}*s{1,Min_Dist_Ctr(k,1)}+s{5,Min_Dist_Ctr(k,2)}*s{1,Min_Dist_Ctr(k,2)})/sumtwoclass;
         % delete original two class centers
         Z([Min_Dist_Ctr(k,1) Min_Dist_Ctr(k,2)],:)=[];
         c=c-1;
      end
   end
end
   J=J+1;
%%%Author: Feng Shuo%%%%%%%%