The vectors that you used to build your covariance matrix all lay in or close to a 3-dimensional subspace of the 4-dimensional space in which they were represented. So one of the eigenvalues of the covariance matrix is 0, or close to it; the matrix is singular. Condition is the ratio of the largest eigenvalue to the smallest, large values can be troublesome. Here it is ~1e17, which is the dynamic range of doubles. Which means that the value you observe for the smallest eigenvaulue is just the result of rounding errors.
w On Wed, 20 Feb 2008, [EMAIL PROTECTED] wrote: >> Different implementations follow different conventions as to which >> is which. > > thank you for the replies ..the reason why i asked was that the most > significant eigenvectors ( sorted according to eigenvalues) are later > used in calculations and then the results obtained differ in java and > python..so i was worried as to which one to use > >> Your matrix is almost singular, is badly conditionned, > > Mathew, can you explain that..i didn't quite get it.. > dn > > _______________________________________________ > Numpy-discussion mailing list > Numpy-discussion@scipy.org > http://projects.scipy.org/mailman/listinfo/numpy-discussion > _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion