[pymvpa] liblapack.so image not found, OS X

[Yaroslav Halchenko]

just for clarity -- both SVD implementations we are talking about are
coming from lapack -- they are just different SVD algorithms present in
lapack.  Numpy chosen one and exposed it in its interface.  Swaroop just
added additional interface to get access to the other one in case lapack
is installed.  Theoretically they both should provide very similar, if
not identical, results.  It  is the corner cases (ill conditioned etc)
where I expect them to be different.  also indeed it would be neat to
know performance wise.


> I also have questions about singular value decomposition convergence
> in hyperalignment and what it means when it does not converge, but
> that is a topic for another thread...

by default it is the first dataset which serves as the 'target' to kick
off the hyperalignment... if SVD implementation happens to not converge,
it tries to use the next dataset and so on... in an unfortunate case
that none of them converged (I bet in case of really obscure data) you
could try different SVD implementation for Procrustean or just look at
WTF "interesting" with your data ;-)


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