sfs.m

自己编的matlab程序。用于模式识别中特征的提取。是特征提取中的Sequential Forward Selection方法

function [nrsfs,fsfs] = sfs(data,cla,nameopt)
% SFS performs the SFS algorithm for training data
%
%   [nrsfs fs] = sfs(data,cla)
%
% The function runs SFS on training data data,and training class cla. 

%
%   data = input of matrix (feature x examples)
%   cla = vector (1 x examples)
%
%   nrsfs = number of right recognition
%   fsfs = vector (select index of feature)


% Author :  MingXian Li  
% Date   :  21.11.2007



if size(data,1)==1
    
    [nr mu_v] = eac(data,cla)
    nrsfs = nr;
    fsfs = 1;
   
    return
end


%feature vector Initialization fv = [0 0 ...0]
fv(1,1:size(data,1)) = 0;
k = 1;


%if select feature i ,dann set fv(i)=1
while sum(fv)<size(data,1)

    j = 1;
    
    for i=1:size(data,1)
        
        sv = fv;
        sv(i) =1;
        
        if (~isequal(sv,fv))
            id = find(sv==1);
%             func = str2func(nameopt);
            [nr mu_v] = feval(nameopt,data(id,:),cla);
            nreac{j} = nr;           
            fvtmp{j} = sv;
            j = j+1;
        end        
                
    end
    

%Afterwards each loop,Compared with Combination, find the best Combination fv. 
    [nr1 index1] = max(cell2mat(nreac));
    fv = cell2mat(fvtmp(index1));
    fvmax{k} = fv;
    nrmax{k} = nr1;    
    k = k+1;
    nreac = [];    
    fvtmp = [];
       
end


%Compared with all best Combination, find the best Combination of feature fvmax{index2}.
[nr2 index2] = max(cell2mat(nrmax));
nrsfs = nr2;
fsfs = find(cell2mat(fvmax(index2))==1);