N Points in an K dimensional Latin hypercube are to be selected. Each of the M coordinate dimensions is discretized to the values 1 through N. The points are to be chosen in such a way that no two points have any coordinate value in common. This is a standard Latin hypercube requirement, and there are many solutions.
This algorithm differs in that it tries to pick a solution which has the property that the points are "spread out" as evenly as possible. It does this by mapping the solution elements through the normal Gaussian cumulative distribution function
X = Generate_LHS('n', 100, 'k' , 2, 'plot_flag',1,'Normal_dist_flag',1,'hist_flag',1);
M. Cavazzuti, Optimization Methods: From Theory to Design,
Springer- Verlag Berlin Heidelberg 2013
Chandramouli Gnanasambandham (2023). Generate Normally Distributed Latin Hyper Cube samples (https://www.mathworks.com/matlabcentral/fileexchange/49675-generate-normally-distributed-latin-hyper-cube-samples), MATLAB Central File Exchange. Retrieved .
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