choosenumberofcomponents.m

I developed an algorithm for using local ICA in denoising multidimensional data. It uses delay embed

function p = choosenumberofcomponents(signal,D,choosemethod,gamma,verbose);
%choosenumberofcomponents - estimate number of components
%
% p = choosenumberofcomponents(signal,D,choosemethod,gamma,gamma);
%
%   choosemethod  = mdl|err|cluster                   (default=mdl)
%
%   gamma                  - (default=1)
%     (gamma<0)            - number of dimensions to use
%     (gamma>0)
%       (choosemethod=mdl) - mdlgamma
%       (choosemethod=err) - portion of signal to discard (0 none, 1 all)
%
% returns the which first p components most likely constitute the signal
%
% 01.08.03 - initial version
% 01.11.04 - rewrite 
% Peter Gruber

error(nargchk(2,5,nargin))     % check no. of input args

[m,samples]=size(signal);

if (nargin<3) choosemethod='mdl'; end
if (nargin<4) gamma=1; end
if (nargin<5) verbose=0; end


V=sum(triu(D'));

if (verbose > 3) plot(D'); title('Variances'); pause; end

p=1;
if (m==1) return; end
switch choosemethod
 case 'mdl'
  p=mdl(D,samples,gamma,verbose);
 case 'err'
  p=m;
  while (sum(V(1,p:m))<gamma*sum(V(1,:))) & (p>0)
   p=p-1; 
  end; 
 case 'abs'
  p=min(ceil(abs(gamma)),m);
 case 'num'
  p=min(max(abs(gamma),0),m);
 case 'cluster'
  if (gamma>0) [ce,cl]=kmeans_cluster('batch',V(1,:)',2); p=sum(cl==cl(1));
  else [ce,cl]=cluster(V,2); p=sum(cl==cl(1)); end
end

return