% Nearest Feature Line Classifier-NFL
function [NFLCrate]=NFLclassifier(features,test_features,trnum,tenum,classnum)

% features       the matrix that training samples projected on feature subspace(特征維數(shù)*訓(xùn)練樣本數(shù)，列矢量)
% test_features  the matrix that test samples projected on feature subspace(特征維數(shù)*測試樣本數(shù)，列矢量) 
% trnum          the number of training samples of each class
% tenum          the number of test samples of each class
% classnum       the number of classes

% NFLCrate       the output correct classification rate of each class and the total
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

% decompose the features and test_features according to the class flag
for i=1:classnum
    subd(:,:,i)=features(:,(i-1)*trnum+1:i*trnum);
    subdd(:,:,i)=test_features(:,(i-1)*tenum+1:i*tenum);
end

% number of NFL of each class
total=(trnum/2)*(trnum-1);
% compute the parameter U (斜率) and the position P (垂點(diǎn))
for m=1:classnum                 % 類別數(shù)
    for t=1:tenum                % 測試樣本數(shù)/類
        k=1;        
        for c=1:classnum         % 類別數(shù)
            for i=1:trnum        % 訓(xùn)練樣本數(shù)/類
                for j=i+1:trnum  % 訓(xùn)練樣本數(shù)/類
                    U=((subdd(:,t,m)-subd(:,i,c))'*(subd(:,j,c)-subd(:,i,c)))/...
                      ((subd(:,j,c)-subd(:,i,c))'*(subd(:,j,c)-subd(:,i,c))+1e-3);
                    P=subd(:,i,c)+U*(subd(:,j,c)-subd(:,i,c));  % 垂足
                    dis(t,k,m)=norm(subdd(:,t,m)-P);            % 計算測試點(diǎn)到垂足的距離
                    k=k+1;       % 計算k條特征線(NFL)
                end
            end
        end       
    end    
end

% compute the correct recognition rate and the according mean value
CCrate=zeros(1,classnum);
for c=1:classnum
    dist=dis(:,:,c)';
    [Y,I]=min(dist);
    k=0;
    for i=1:tenum
        if ceil(I(i)/total)==c
            k=k+1;
        end
    end
    CCrate(c)=k/tenum;            
end
rtmean=sum(CCrate)/classnum;

% output
NFLCrate=zeros(1,classnum+1);
NFLCrate=[CCrate,rtmean];