Python Nearly Orthogonal Latin Hypercube Generator. For each column, the n values are randomly distributed with one from each interval (0,1/n), (1/n,2/n). This library allows to generate Nearly Orthogonal Latin Hypercubes (NOLH) according to Cioppa (2007) and De Rainville et al. , (1-1/n,1), and they are randomly permuted. were responsible for adapting for our purposes the LHS/PRCC codes originally created by Dr. Iteratively generates latin hypercube samples to find the best one according to the criterion ' c', which can be: Produces points at the midpoints of the above intervals: 0.5/n, 1.5/n. Iterates up to k times in an attempt to improve the design according to the specified criterion. These estimates were compared against numerical results based on a MATLAB imple. In the present paper the Matlab Latin Hypercube sampling routine, which has implemented a function that attempts to optimize the sample with respect to an optimum euclidean distance between design points, is used. If you want integers only in the sample, then we must be careful about what we mean by a Latin hypercube sample. If you wanted exactly 3 points, then you could divide up the range into three almost equal parts and sample from 1:3, 4:6, and 7:10. (7 would be sampled less often than 2 for example) The problem is that it wouldn’t be uniform sample across the range. Matlab latin hypercube sampling code code#.
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