[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.

w

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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