Center the points within the sampling intervals. Five criteria for the construction of LHS are implemented in SMT: Featured on Meta Unpinning the accepted answer from the top of the list of answersThe LHS method uses the pyDOE package (Design of Experiments for Python). LHS is built as follows: we cut each dimension space, which represents a variable, into nExample: X lhsdesign(n,p,'Smooth','off') returns a discrete Latin hypercube design Smooth Indication for continuous samples 'on' (default) 'off' Indication for continuous samples, specified as the comma-separated pair consisting of 'Smooth' and 'on' (continuous samples) or 'off' (discrete samples).Sections where n is the number of sampling points, and we put only one point in each section.Browse other questions tagged sampling latin-hypercube or ask your own question. It is among the most popular sampling techniques in computer experiments thanks to its simplicity and projection properties with high-dimensional problems.

(2005), вЂњAn efficient algorithm for constructing optimal design of computer experiments.вЂќ Journal of Statistical Planning and Inference, 134:268-287.

Latin Hypercube Sampling Python Example