[Numpy-discussion] finding eigenvectors etc

Warren Focke focke@slac.stanford....
Wed Feb 20 02:27:00 CST 2008

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.


On Wed, 20 Feb 2008, devnew@gmail.com 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
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