[pymvpa] PyMVPA Question

[Taku Ito]

Hi,

I'm currently running through PyMVPA my first time, and ran into question I
couldn't quite figure out straightforwardly through the manual.

The data set that I have currently has only two target labels, but the
number of labels for each of the targets are unequal.  I'm curious to try
and find a way to train and test the classifier on N-Fold subsets, where
each subsets have an equal number of labels for each target value.
I'm currently constructing my CrossValidation object as such:

clf = LinearCSVMC()
cvte = CrossValidation(clf, NFoldPartitioner(),
                                errorfx=lambda p, t: np.mean(p == t),
                                enable_ca=['stats'])

I was thinking that maybe the 3rd parameter in the documentation, the
'Splitter' instance may be useful, but I was confused since the
documentation seems to indicate that it would split the attributes based on
the labels...  I also came across NFoldSplitter during my search (which
looked promising), but it seemed as if that object was no longer supported.
 Please let me know if you might have any insights!

Thanks,
Taku
-- 
Takuya Ito
Cognitive Control & Psychopathology Laboratory
Washington University in St. Louis

Cole Neuroscience Laboratory
Center for Molecular and Behavioral Neuroscience (CMBN)
Rutgers-Newark University
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