[pymvpa] Recovering original image dimensions after remove_invariant_features

[Nick Oosterhof]

On 23 Oct 2014, at 16:27, Yaroslav Halchenko <debian at onerussian.com> wrote:

> 
> On Tue, 21 Oct 2014, Shane Hoversten wrote:
> 
>>   Hi Nick -
>>   Thanks for the reply.
>>   An update: for the time being I just went back to not using
>>   remove_invariant_features and ignoring those warning messages.A  However,
>>   processing one of my subject's data (so far only just one hangs, out of 7
>>   that finish) hangs PyMVPA s.t. it never resolves.A  My understanding is
>>   that these warnings I'm getting:
>>   WARNING: Obtained degenerate data with zero norm for training of
>>   <LinearCSVMC>.A  Scaling of C cannot be done.
> 
> sounds more like a degenerate sample somewhere

Yes, that’s my impression too.

If we were to add a mapper (StaticFeatureSelection to be precise) to the dataset when removing invariant features, then this would get rid of the degenerate samples *and* cause no issues when mapping the data back to NIFTI.

The question (for developers): would it be better to add a new function for this, or rewrite the current remove_invariant_features?