Generate Normally Distributed Latin Hyper Cube samples

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

Example :
X = Generate_LHS('n', 100, 'k' , 2, 'plot_flag',1,'Normal_dist_flag',1,'hist_flag',1);

References:
M. Cavazzuti, Optimization Methods: From Theory to Design,
Springer- Verlag Berlin Heidelberg 2013

Inspired by:
http://www.mathworks.com/matlabcentral/fileexchange/48927-lhsdesigncon