resampstr.m

function s = resampstr(p);
%RESAMPSTR Stratified resampling
%   S = RESAMPSTR(P) returns a new set of indices according to 
%   the probabilities P. P is array of probabilities, which are
%   not necessarily normalized, though they must be non-negative,
%   and not all zero. The size of S is the size of P.
%
%   Default is to use no-sort resampling. For sorted resampling use
%    [PS,PI]=SORT(P);
%    S=PI(RESAMPSTR(PS));
%   Sorted re-sampling is slower but has slightly smaller variance.
%   Stratified resampling is unbiased, almost as fast as
%   deterministic resampling (RESAMPDET), and has only slightly
%   larger variance.
%
%   In stratified resampling indices are sampled using random
%   numbers u_j~U[(j-1)/n,j/n], where n is length of P. Compare
%   this to simple random resampling where u_j~U[0,1]. See,
%   Kitagawa, G., Monte Carlo Filter and Smoother for
%   Non-Gaussian Nonlinear State Space Models, Journal of
%   Computational and Graphical Statistics, 5(1):1-25, 1996. 
%
%   See also RESAMPSIM, RESAMPRES, RESAMPDET

% Copyright (c) 2003-2004 Aki Vehtari
%
% This software is distributed under the GNU General Public 
% Licence (version 2 or later); please refer to the file 
% Licence.txt, included with the software, for details.

[m,n]=size(p);
mn=m.*n;
pn=p./sum(p(:)).*mn;
fpn=floor(pn);
s=zeros(m,n);
r=rand(1,mn);
k=0;
c=0;
for i=1:mn
  c=c+pn(i);
  if c>=1
    a=floor(c);
    c=c-a;
    s(k+[1:a])=i;
    k=k+a;
  end
  if k<mn && c>=r(k+1)
    c=c-1;
    k=k+1;
    s(k)=i;
  end
end