Hi Scott: You are correct on all accounts. epsilon is currently hard-coded and either of your proposed solutions would help to make GPR work more frequently/consistently. I'll try out some combination of both and report back.
Best, Per On Sat, Mar 28, 2009 at 9:46 AM, Scott Gorlin <[email protected]> wrote: > > Epsilon is the Tikhonov regularization parameter, no? (Haven't read > much about this classifier, but it looks similar to the RLS solution to > me...). As such it should be adjustable to enable punishing overfit > solutions, so maybe it should actually be a formal parameter. > > If the default returns a LinAlg error, you could also stick the cholesky > decomposition in a finite loop where epsilon is doubled each time > (though not too many iterations...) > > On a wild tangent, also check that your kernel matrix (here self._C) is > double precision, since 1e-20 is close to the precision limit for single > floats. > > Per B. Sederberg wrote: >> Hi Everybody (and Emanuele in particular): >> >> I've tried GPR on about 5 different datasets and I've never actually >> gotten it to run through an entire cross validation process. I always >> get the following error: >> >> /home/per/lib/python/mvpa/clfs/gpr.pyc in _train(self, data) >> 308 except SLAError: >> 309 epsilon = 1.0e-20 * N.eye(self._C.shape[0]) >> --> 310 self._L = SLcholesky(self._C + epsilon, lower=True) >> 311 self._LL = (self._L, True) >> 312 pass >> >> /usr/lib/python2.5/site-packages/scipy/linalg/decomp.pyc in >> cholesky(a, lower, overwrite_a) >> 552 potrf, = get_lapack_funcs(('potrf',),(a1,)) >> 553 c,info = potrf(a1,lower=lower,overwrite_a=overwrite_a,clean=1) >> --> 554 if info>0: raise LinAlgError, "matrix not positive definite" >> 555 if info<0: raise ValueError,\ >> 556 'illegal value in %-th argument of internal potrf'%(-info) >> >> LinAlgError: matrix not positive definite >> >> I think Yarik mentioned changing epsilon or something like that, but >> it seems to me that having to change source code to tweak a classifier >> to not crash is not ideal. >> >> Do you have any suggestions for how to fix this? I've been tantalized >> with some very good transfer errors in the folds that GPR was able to >> complete before crashing. Is there a permanent change we could make >> to the GPR code to make it more stable across datasets? Or could we >> turn epsilon into a configurable variable when setting up the >> classifier? If so, what should I change epsilon to in order to see if >> I can get it to run? >> >> Thanks, >> Per >> >> _______________________________________________ >> Pkg-ExpPsy-PyMVPA mailing list >> [email protected] >> http://lists.alioth.debian.org/mailman/listinfo/pkg-exppsy-pymvpa >> > > > > _______________________________________________ > Pkg-ExpPsy-PyMVPA mailing list > [email protected] > http://lists.alioth.debian.org/mailman/listinfo/pkg-exppsy-pymvpa > _______________________________________________ Pkg-ExpPsy-PyMVPA mailing list [email protected] http://lists.alioth.debian.org/mailman/listinfo/pkg-exppsy-pymvpa
