mrmr_miq_d.m

function [fea] = mrmr_miq_d(d, f, K)
% function [fea] = mrmr_miq_d(d, f, K)
% 
% The MIQ scheme of minimum redundancy maximal relevance (mRMR) feature selection
% 
% The parameters:
%  d - a N*M matrix, indicating N samples, each having M dimensions. Must be integers.
%  f - a N*1 matrix (vector), indicating the class/category of the N samples. Must be categorical.
%  K - the number of features need to be selected
%
% Note: This version only supports discretized data, thus if you have continuous data in "d", you 
%       will need to discretize them first. This function needs the mutual information computation 
%       toolbox written by the same author, downloadable at the Matlab source codes exchange site. 
%       Also There are multiple newer versions on the Hanchuan Peng's web site 
%       (http://research.janelia.org/peng/proj/mRMR/index.htm).
%
% More information can be found in the following papers. 
%
% H. Peng, F. Long, and C. Ding, 
%   "Feature selection based on mutual information: criteria 
%    of max-dependency, max-relevance, and min-redundancy,"
%   IEEE Transactions on Pattern Analysis and Machine Intelligence,
%   Vol. 27, No. 8, pp.1226-1238, 2005. 
%
% C. Ding, and H. Peng, 
%   "Minimum redundancy feature selection from microarray gene 
%    expression data,"  
%    Journal of Bioinformatics and Computational Biology,
%   Vol. 3, No. 2, pp.185-205, 2005. 
%
% C. Ding, and H. Peng, 
%   "Minimum redundancy feature selection from microarray gene 
%    expression data,"  
%   Proc. 2nd IEEE Computational Systems Bioinformatics Conference (CSB 2003),
%   pp.523-528, Stanford, CA, Aug, 2003.
%  
%
%
% By Hanchuan Peng (hanchuan.peng@gmail.com)
% April 16, 2003
%

bdisp=0;

nd = size(d,2);
nc = size(d,1);

t1=cputime;
for i=1:nd, 
   t(i) = mutualinfo(d(:,i), f);
end; 
fprintf('calculate the marginal dmi costs %5.1fs.\n', cputime-t1);

[tmp, idxs] = sort(-t);
fea_base = idxs(1:K);

fea(1) = idxs(1);

KMAX = min(1000,nd); %500 %20000

idxleft = idxs(2:KMAX);

k=1;
if bdisp==1,
fprintf('k=1 cost_time=(N/A) cur_fea=%d #left_cand=%d\n', ...
      fea(k), length(idxleft));
end;

for k=2:K,
   t1=cputime;
   ncand = length(idxleft);
   curlastfea = length(fea);
   for i=1:ncand,
      t_mi(i) = mutualinfo(d(:,idxleft(i)), f); 
      mi_array(idxleft(i),curlastfea) = getmultimi(d(:,fea(curlastfea)), d(:,idxleft(i)));
      c_mi(i) = mean(mi_array(idxleft(i), :)); 
   end;

%   [tmp, fea(k)] = max(t_mi(1:ncand) ./ c_mi(1:ncand));
   [tmp, fea(k)] = max(t_mi(1:ncand) ./ (c_mi(1:ncand) + 0.01));

   tmpidx = fea(k); fea(k) = idxleft(tmpidx); idxleft(tmpidx) = [];

   if bdisp==1,
   fprintf('k=%d cost_time=%5.4f cur_fea=%d #left_cand=%d\n', ...
      k, cputime-t1, fea(k), length(idxleft));
   end;
end;

return;

%===================================== 
function c = getmultimi(da, dt) 
for i=1:size(da,2), 
   c(i) = mutualinfo(da(:,i), dt);
end;