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X = lhsdesign(n,p)
X = lhsdesign(...,'smooth','off')
X = lhsdesign(...,'criterion',criterion)
X = lhsdesign(...,'iterations',k)
X = lhsdesign(n,p) generates a latin hypercube sample X containing n values on each of p variables. For each column, the n values are randomly distributed with one from each interval (0,1/n), (1/n,2/n), ..., (1-1/n,1), and they are randomly permuted.
X = lhsdesign(...,'smooth','off') produces points at the midpoints of the above intervals: 0.5/n, 1.5/n, ..., 1-0.5/n. The default is 'on'.
X = lhsdesign(...,'criterion',criterion) iteratively generates latin hypercube samples to find the best one according to the criterion criterion, which can be one of the following strings.
| Criterion | Description |
|---|---|
'none' | No iteration |
'maximin' | Maximize minimum distance between points |
'correlation' | Reduce correlation |
X = lhsdesign(...,'iterations',k) iterates up to k times in an attempt to improve the design according to the specified criterion. The default is k = 5.
haltonset, sobolset, lhsnorm, unifrnd
![]() | leverage | lhsnorm | ![]() |

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