Hi,
i have two questions, both loosely related to SVD.
I've seen this post:
http://thread.gmane.org/gmane.comp.python.numeric.general/4575
>>> u,s,v = numpy.linalg.svd(numpy.array([[4,2],[2,4]])) # symmetric
matrix u == v
>>> u
array([[-0.70710678, -0.70710678],
[-0.70710678, 0.70710678]])
>>> v
array([[-0.70710678, -0.70710678],
[-0.70710678, 0.70710678]])
>>> s.shape
(2,)
since my data matrix is symmetrical, i'd expect USV = X, but I don't get that:
>>> u * s * v
array([[ 3., 1.],
[ 3., 1.]])
matrixmultiply doesnt help either
>>> from numpy.core import matrixmultiply as mm
>>> mm(u,mm(s,v))
array([ 6., 2.])
Question 2.
I'm relativly new to linealg, so i could be way off here.
In applications such as LSA, the dimensions of a matrix are either
documents or term identifiers, I noticed in PDL ( http://pdl.perl.org/
), you can set the headers of row or columns.
I havent found a way to do this in numpy, which means when the
dimensions get sorted by their singular value, I lose the ordering I
may have recorded externally.
I there a way to store row and column headers?
Cheers
David Novakovic
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