load IrisDataX;
X=X';
[nr,nc]=size(X);

T=[ones(1,50), zeros(1,100);
  zeros(1,50), ones(1,50), zeros(1,50);
  zeros(1,100), ones(1,50)];

hatX=[X;ones(1,nc)];
hatW=T/hatX;

Y=hatW*hatX;             %Output the classifier
[vals,idx]=max(Y,[],1);  %Tell us which class 
[vals,idx2]=max(T,[],1); %For comparison purposes

Classes=zeros(3,1);  % Keeps track of how many in each class
Confusion=zeros(3,3);
for j=1:nc
   Confusion(idx(j),idx2(j))=Confusion(idx(j),idx2(j))+1;
   Classes(idx2(j))=Classes(idx2(j))+1;
end

%To get percentages, divide each column by its number of classes:
Confusion=Confusion./repmat(Classes',3,1);