I think that is correct...

Here is what the final result should look like:

http://www.datawrangling.com/media/images/first_16.png

If the dimensions for the sample faces don't work out to ( 361 x 361 ) in the end, then you are likely to be missing a transpose somewhere. Also, be aware that the scipy linalg.eig by default returns a vector of eigenvalues and a matrix, but the Matlab eig(), returns 2 matrices ( the eigenvalues are multiplied by an identity matrix to get a diagonal matrix). You can check out the mathesaurus reference sheet for help translating the example into python, but hopefully this will point you in the right direction:

see:

http://www.mathworks.com/access/helpdesk/help/techdoc/ref/eig.html

vs:

>>> help(linalg.eig)

Help on function eig in module scipy.linalg.decomp:

eig(a, b=None, left=False, right=True, overwrite_a=False, overwrite_b=False)
    Solve ordinary and generalized eigenvalue problem
    of a square matrix.

    Inputs:

      a     -- An N x N matrix.
      b     -- An N x N matrix [default is identity(N)].
      left -- Return left eigenvectors [disabled].
      right -- Return right eigenvectors [enabled].
      overwrite_a, overwrite_b -- save space by overwriting the a and/or
                                  b matrices (both False by default)

    Outputs:

      w      -- eigenvalues [left==right==False].
      w,vr   -- w and right eigenvectors [left==False,right=True].
      w,vl   -- w and left eigenvectors [left==True,right==False].
      w,vl,vr -- [left==right==True].

    Definitions:

      a * vr[:,i] = w[i] * b * vr[:,i]

      a^H * vl[:,i] = conjugate(w[i]) * b^H * vl[:,i]

    where a^H denotes transpose(conjugate(a)).

On Sat, Mar 1, 2008 at 12:41 AM, devnew@gmail.com <devnew@gmail.com> wrote:

On Mar 1, 12:57 am, "Peter Skomoroch" wrote:
I think
> > matlab example should be easy to translate to scipy/matplotlib using the
> > montage function:
>
> > load faces.mat
> > %Form covariance matrix
> > C=cov(faces');
> > %build eigenvectors and eigenvalues
> > [E,D] = eig(C);

hi Peter,
nice code..ran the examples..
however couldn't follow the matlab code since i have no exposure to
matlab..was using numpy etc for calcs
could you confirm the layout for the face images data? i assumed that
the initial face matrix should be
faces=a numpy matrix with N rows ie N=numofimages

row1=image1pixels as a sequence
row2=image2pixels as a sequence
...
rowN=imageNpixels as a sequence

and covariancematrix=faces*faces_transpose

is this the right way?
thanks

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--
Peter N. Skomoroch
peter.skomoroch@gmail.com
http://www.datawrangling.com