On Fri, May 05, 2017 at 12:13:08PM +0200, Alle Meije Wink wrote:
> Thanks for that Gael - I do know nilearn but in this case I did a depth-first
> search on doing SVM on brain images and ended up here :)
:). Darn, we need to work on our search engine optimization. Nilearn
should be the easiest way
Thanks for that Gael - I do know nilearn but in this case I did a
depth-first search on doing SVM on brain images and ended up here :)
The size of 'coef' is (1,205739), the size of mask[mask].T is (205739,)
Same number of elements, different storage layout(?).
Turned out that the numpy.ravel() fun
Hi Alle,
I think that what has changed between 2014 and today is that the
coefficients coef are now a 2D array (number of hyperplanes x number of
features). In your case, the first direction is of length one, so you
could just do:
coef = clf.coef_[0]
and your script should work.
The code of the
I have a script to classify MRI perfusion maps from healthy subjects and
patients. For the file IO and the classifier I have started with the
example code in Abraham et al 2014 [https://arxiv.org/pdf/1412.3919.pdf].
I use the same classifier as in the paper to produce a back-projected map
of class
