This generates m random n-element column vectors of values, [x1;x2;...;xn], each with a fixed sum, s, and subject to a restriction a<=xi<=b. The vectors are randomly and uniformly distributed in the n-1 dimensional space of solutions. This is accomplished by decomposing that space into a number of different types of simplexes (the many-dimensional generalizations of line segments, triangles, and tetrahedra.) The 'rand' function is used to distribute vectors within each simplex uniformly, and further calls on 'rand' serve to select different types of simplexes with probabilities proportional to their respective n-1 dimensional volumes. This algorithm does not perform any rejection of solutions - all are generated so as to already fit within the prescribed hypercube.
i am trying to generate 6 random nmbrs within given range and sum:
xmin=[10 10 40 35 130 125];
xmax=[125 150 250 210 325 315];
Pg=randfixedsum(1,6,200,xmin, xmax);
it is giving following error:
?? Error using ==> minus
Matrix dimensions must agree.
Error in ==> randfixedsum at 56
s1 = s - (k:-1:k-n+1); % s1 & s2 will never be negative
Error in ==> busdatas at 47
Qg=randfixedsum(30,1,total(8),xmin, xmax);
Nice. I'm trying to generate random data within a simplex defined by linear inequality constraints.
Lets say I already have the N vertices of the simplex defined by the inequalities. Is it then correct to first generate a random sample in the interval [0,1] with a sum equal to 1, and then take the inner product of this sample with the vector of vertices?
Something along the lines of:
X = rand(6,2);
k = convhull(X);
plot(X(k,1),X(k,2),'b'), hold on
nv = numel(k)-1; % Nmuber of vertices
X = X(k(1:end-1),:); % Remove repeated first vertex
L = randfixedsum(size(X,1),1000,1,0,1);
Y = L'*X;
plot(Y(:,1),Y(:,2),'r.'), hold off
Maybe I shouldn't trust my vision on this, but the samples don't really look uniformly spread within the simplex. For some reason they only seem to do for a triangle.
Exactly what I was looking for!!! Many thanks for the great work!!!
03 Apr 2007
Per-Anders Ekström
Excellent!
01 Sep 2006
Mike Edwards
very useful! beautiful code!
30 Jan 2006
John D'Errico
This took a bit of work to verify uniformity in a slice of an n-dimensional hypercube. I'm now confident that Roger has done what he claimed, having checked samplings in several different dimensions, as well as having thought through the process he used to generate the sampling.