Simple Matlab Random Number Generation
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I have to get 5 random numbers a1, a2, a3, a4, a5 where each a1, a2, a3, a4, a5 should be between [-0.5, 0.5] and sum i.e. a1 + a2 + a3 + a4 + a5 = 1.
How should I do it?
4 comentarios
Paulo Silva
el 28 de Feb. de 2011
I deleted my answer (the one that was accepted but it wasn't the best one) and voted on Bruno's and Matt's answers.
Please reselect (Sam or someone who can (admins?!)) the best answer, thank you.
Respuesta aceptada
chris hinkle
el 27 de Feb. de 2011
Create 5 arrays that have all possible combinations of these numbers then generate a random number that is between 1 and length of array and then use that value as the index for the array and viola, there's your number. The size of these arrays can be controlled by the resolution you go to.
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Más respuestas (2)
Bruno Luong
el 27 de Feb. de 2011
To generate true uniform distribution, the correct method is not quite straightforward. I strongly recommend Roger Stafford's FEX,
http://www.mathworks.com/matlabcentral/fileexchange/9700-random-vectors-with-fixed-sum
3 comentarios
the cyclist
el 27 de Feb. de 2011
Agreed that this is the definitive answer. Specifically for Sam's solution:
X = randfixedsum(5,10000,1,-0.5,0.5);
Matt Tearle
el 27 de Feb. de 2011
How about a brute-force approach?
ntot = 0;
n = 10000;
x = zeros(n,5);
while ntot<n
r = rand(100,4)-0.5;
r5 = 1 - sum(r,2);
idx = (r5>-0.5) & (r5<0.5);
tmp = [r(idx,:),r5(idx)];
nidx = min(size(tmp,1),n-ntot);
x(ntot+1:ntot+nidx,:) = tmp(1:nidx,:);
ntot = ntot + nidx;
end
1 comentario
the cyclist
el 28 de Feb. de 2011
My first reaction to this solution was that, as a rejection method (with a loop, no less!), it would be much slower than Roger's method. The reality is that is does comparably well, speed-wise. I haven't done a full-blown comparison, but I think the reason is two-fold. First, you "semi-vectorized" by pulling chunks of random numbers at a time. Second, and I think more importantly, the accept/reject fraction is pretty good. (It might not be so favorable otherwise, like if the marginals were on [0,1] and still had to sum to 1.)
This solution is highly intuitive, and I believe leads to marginal distributions and correlations between summands that are identical to Roger's solution.
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