diff toolboxes/FullBNT-1.0.7/KPMstats/sample_discrete.m @ 0:cc4b1211e677 tip

initial commit to HG from
Changeset:
646 (e263d8a21543) added further path and more save "camirversion.m"

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/FullBNT-1.0.7/KPMstats/sample_discrete.m	Fri Aug 19 13:07:06 2016 +0200
@@ -0,0 +1,40 @@
+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);