Generate random vectors in PCA subspace. Used to generate random vectors from the subspace spanned by the first k principal components. The points generated come from the gaussian distribution from within the subspace. Can optionally generate points on the subspace that are also on a hypershpere centered on the origin. This may be useful if the original data points were all from a hypershpere -- for example they were normalized via imnormalize. Set the optional hypershpere flag to 1 to generate points only on the hypersphere. INPUTS U - [returned by pca] -- see pca mu - [returned by pca] -- see pca variances - [returned by pca] -- see pca k - number of principal coordinates to use n - number of x to generate hypershpere - [optional] generate points on hypersphere (see above) show - [optional] figure to use for display (no display if == 0) OUTPUTS Xr - resulting randomly generated vectors DATESTAMP 29-Nov-2005 2:00pm See also PCA