[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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