"Jaidev Deshpande" <[email protected]> wrote
I tried using the 'scipy.sparse.eigs' tool for performing principal
component analysis on a matrix which is roughly 80% sparse.
First of all, is that a good way to go about it?
Umm, I remember the term eigenvalue from University math.
Other than that the first sentence could be in Chinese! :-)
I suspect you might get a better resoponse on a scipy
or numpy mailing list or forum. Although there are a few
users of that here it sounds like your queries are likely
to be more scipy oriented than general Python.
There is a list of options here:
http://www.scipy.org/Mailing_Lists
Second, the operation failed when the function failed to converge on
accurate eigenvalues. I noticed the 'tol' attribute in the function,
but how
does one define a reasonable tolerance and calculate it?
Sorry, I understand the sentence this time but again
its more a scipy question than a Python one.
Alan G.
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