comparison toolboxes/FullBNT-1.0.7/KPMtools/sample_discrete.m @ 0:e9a9cd732c1e tip

first hg version after svn
author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
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-1:000000000000 0:e9a9cd732c1e
1 function M = sample_discrete(prob, r, c)
2 % SAMPLE_DISCRETE Like the built in 'rand', except we draw from a non-uniform discrete distrib.
3 % M = sample_discrete(prob, r, c)
4 %
5 % Example: sample_discrete([0.8 0.2], 1, 10) generates a row vector of 10 random integers from {1,2},
6 % where the prob. of being 1 is 0.8 and the prob of being 2 is 0.2.
7
8 n = length(prob);
9
10 if nargin == 1
11 r = 1; c = 1;
12 elseif nargin == 2
13 c == r;
14 end
15
16 R = rand(r, c);
17 M = ones(r, c);
18 cumprob = cumsum(prob(:));
19
20 if n < r*c
21 for i = 1:n-1
22 M = M + (R > cumprob(i));
23 end
24 else
25 % loop over the smaller index - can be much faster if length(prob) >> r*c
26 cumprob2 = cumprob(1:end-1);
27 for i=1:r
28 for j=1:c
29 M(i,j) = sum(R(i,j) > cumprob2)+1;
30 end
31 end
32 end
33
34
35 % Slower, even though vectorized
36 %cumprob = reshape(cumsum([0 prob(1:end-1)]), [1 1 n]);
37 %M = sum(R(:,:,ones(n,1)) > cumprob(ones(r,1),ones(c,1),:), 3);
38
39 % convert using a binning algorithm
40 %M=bindex(R,cumprob);