![]() I actually would recommend you to use a number of samples as close as possible to the maximum of 1296. You wish 1000 samples, that's a realistic number and can be done in a reasonable time. X is finally containing the coordinates of all your samples.Īs you see I used a while-loop with an underlying if-condition. % the perfect LHC distribution would have 1296 samples for M=6 divisions % For at least 1000 samples M=6 divisions are necessary The following solution is far from fast and elegant, but it's at least a solution. So for this case I still provide my initial idea here: I tried to modify the approach above D = round(bsxfun.), but it won't give you satisfying results. X = lhsdesign(p,N,'criterion','correlation') ĭ = would give you the desired "equal distribution".ĭ then contains the irregular coordinate-distribution for your parameters.įirst I thought you we're looking for samples on a regular grid, which really seems to be a tough task. If not, you could use the built-in function lhsdesign: p = 1000 % Number of points The basic question is whether you want your samples on a regular grid or not. ![]() UPDATE #2: solution using built-in function of Statistics Toolbox
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