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Copy pathknnclassify.m
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knnclassify.m
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function [Eval,Details]=knnclassify(Min_L,Min_xTr,Min_lTr,Min_xTe,Min_lTe,KK);
% function [Eval,Details]=knnclassify(Min_L,Min_xTr,Min_lTr,Min_xTe,Min_lTe,KK);
%
% INPUT:
% Min_L : transformation matrix Low-d x High-d
% Min_xTr : training vectors (each row is an instance)
% Min_yTr : training labels (Colum vector!!)
% Min_xTe : test vectors
% Min_yTe : test labels
% KK : number of nearest neighbors
%
% Good luck!
%
% copyright by Kilian Q. Weinberger, 2006
%
% version 1.1 (04/13/07)
% Little bugfix, couldn't handle single test vectors beforehand.
% Thanks to Karim T. Abou-Moustafa for pointing it out to me.
% Modifed by Renqiang Min (02/2008)
L = Min_L;
xTr = Min_xTr';
lTr = Min_lTr';
xTe = Min_xTe';
lTe = Min_lTe';
MM=min([lTr lTe]);
if(nargin<6)
KK=1:2:3;
end;
if(length(KK)==1) outputK=ceil(KK/2);KK=1:2:KK;else outputK=1:length(KK);end;
Kn=max(KK);
D=length(L);
xTr=L*xTr(1:D,:);
xTe=L*xTe(1:D,:);
B=2000;
[NTr]=size(xTr,2);
[NTe]=size(xTe,2);
Eval=zeros(2,length(KK));
lTr2=zeros(length(KK),NTr);
lTe2=zeros(length(KK),NTe);
iTr=zeros(Kn,NTr);
iTe=zeros(Kn,NTe);
sx1=sum(xTr.^2,1);
sx2=sum(xTe.^2,1);
for i=1:B:max(NTr,NTe)
if(i<=NTr)
BTr=min(B-1,NTr-i);
%Dtr=distance(xTr,xTr(:,i:i+BTr));
Dtr=addh(addv(-2*xTr'*xTr(:,i:i+BTr),sx1),sx1(i:i+BTr));
% [dist,nn]=sort(Dtr);
[dist,nn]=mink(Dtr,Kn+1);
nn=nn(2:Kn+1,:);
lTr2(:,i:i+BTr)=LSKnn2(lTr(nn),KK,MM);
iTr(:,i:i+BTr)=nn;
Eval(1,:)=sum((lTr2(:,1:i+BTr)~=repmat(lTr(1:i+BTr),length(KK),1))',1)./(i+BTr);
end;
if(i<=NTe)
BTe=min(B-1,NTe-i);
Dtr=addh(addv(-2*xTr'*xTe(:,i:i+BTe),sx1),sx2(i:i+BTe));
[dist,nn]=mink(Dtr,Kn);
lTe2(:,i:i+BTe)=LSKnn2(reshape(lTr(nn),max(KK),BTe+1),KK,MM);
iTe(:,i:i+BTe)=nn;
Eval(2,:)=sum((lTe2(:,1:i+BTe)~=repmat(lTe(1:i+BTe),length(KK),1))',1)./(i+BTe);
end;
% fprintf('%2.2f%%.:\n',(i+BTr)/max(NTr,NTe)*100);
% disp(Eval.*100);
end;
Details.lTe2=lTe2;
Details.lTr2=lTr2;
Details.iTe=iTe;
Details.iTr=iTr;
Eval=Eval(:,outputK);
function yy=LSKnn2(Ni,KK,MM);
% function yy=LSKnn2(Ni,KK,MM);
%
if(nargin<2)
KK=1:2:3;
end;
N=size(Ni,2);
Ni=Ni-MM+1;
classes=unique(unique(Ni))';
%yy=zeros(1,size(Ni,2));
%for i=1:size(Ni,2)
% n=zeros(max(un),1);
% for j=1:size(Ni,1)
% n(Ni(j,i))=n(Ni(j,i))+1;
% end;
% [temp,yy(i)]=max(n);
%end;
T=zeros(length(classes),N,length(KK));
for i=1:length(classes)
c=classes(i);
for k=KK
% NNi=Ni(1:k,:)==c;
% NNi=NNi+(Ni(1,:)==c).*0.01;% give first neighbor tiny advantage
try
T(i,:,k)=sum(Ni(1:k,:)==c,1);
catch
keyboard;
end;
end;
end;
yy=zeros(max(KK),N);
for k=KK
[temp,yy(k,1:N)]=max(T(:,:,k)+T(:,:,1).*0.01);
yy(k,1:N)=classes(yy(k,:));
end;
yy=yy(KK,:);
yy=yy+MM-1;