Hi,
The opposite of an eigenvector is an eigenvector as well, with the same
eigenvalue. Depending on the algorithm, both can be returned.
Matthieu
2008/5/3 wilson <[EMAIL PROTECTED]>:
> thanks for the links..
> but why the different signs for entries in eigenvectors? is it a
> library specific
thanks for the links..
but why the different signs for entries in eigenvectors? is it a
library specific thing? shouldn't they be identical?
W
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On Fri, 2 May 2008 23:34:19 -0700 (PDT)
wilson <[EMAIL PROTECTED]> wrote:
> I am trying out the eigenvectors related functions in
>numpy.linalg.I
> came across some portions where i have doubts.
> 1).
> i have an array X
> if i calculate L=dot(X,X.transpose())
> can L be called the covariance m
I am trying out the eigenvectors related functions in numpy.linalg.I
came across some portions where i have doubts.
1).
i have an array X
if i calculate L=dot(X,X.transpose())
can L be called the covariance matrix of X?I read so in a paper by
Turk&Pentland(equation 3 i think)
can someone clarify t
On Sat, Mar 1, 2008 at 2:43 PM, [EMAIL PROTECTED] <[EMAIL PROTECTED]> wrote:
> hi
> i have a set of images of faces which i make into a 2d array using
> numpy.ndarray
> each row represents a face image
> faces=
> [[ 173. 87. ... 88. 165.]
> [ 158. 103. .. 73. 143.]
> [ 180. 87
hi
i have a set of images of faces which i make into a 2d array using
numpy.ndarray
each row represents a face image
faces=
[[ 173. 87. ... 88. 165.]
[ 158. 103. .. 73. 143.]
[ 180. 87. .. 55. 143.]
[ 155. 117. .. 93. 155.]]
from which i can get the mean image =>
avgface=a
