view _FullBNT/KPMtools/sample_discrete.m @ 9:4ea6619cb3f5 tip

removed log files

function M = sample_discrete(prob, r, c)
% SAMPLE_DISCRETE Like the built in 'rand', except we draw from a non-uniform discrete distrib.
% M = sample_discrete(prob, r, c)
%
% Example: sample_discrete([0.8 0.2], 1, 10) generates a row vector of 10 random integers from {1,2},
% where the prob. of being 1 is 0.8 and the prob of being 2 is 0.2.

n = length(prob);

if nargin == 1
  r = 1; c = 1;
elseif nargin == 2
  c == r;
end

R = rand(r, c);
M = ones(r, c);
cumprob = cumsum(prob(:));

if n < r*c
  for i = 1:n-1
    M = M + (R > cumprob(i));
  end
else
  % loop over the smaller index - can be much faster if length(prob) >> r*c
  cumprob2 = cumprob(1:end-1);
  for i=1:r
    for j=1:c
      M(i,j) = sum(R(i,j) > cumprob2)+1;
    end
  end
end


% Slower, even though vectorized
%cumprob = reshape(cumsum([0 prob(1:end-1)]), [1 1 n]);
%M = sum(R(:,:,ones(n,1)) > cumprob(ones(r,1),ones(c,1),:), 3);

% convert using a binning algorithm
%M=bindex(R,cumprob);