[pymvpa] custom cross-validation procedure: train on individual blocks, test on averaged blocks?

[e c]

Hi all,

I would like to do the following cross-validation procedure in pymvpa. Here is my toy example: Say I have 3 runs in a block-design experiment. I have two conditions, A and B, and in each run I have 3 blocks of each condition. E.g.:

Run 1: A A B A B B
Run 2: A A A B B B
Run 3: A A B B A B

I would like to do a leave-one-out classification, but on each fold, I would like to train on individual blocks, and test on averaged blocks in the left out run. So I feed individual blocks of 'A' and 'B' from two runs to train the classifier, but on the left out run,  I average all the 'A's and 'B's, and test the classifier on each of these. So I test the classifier twice instead of 6 times on each fold.

How do I do this? Is this possible by just using the CrossValidation() function? Or do I have to rewrite it...

Thanks!

-Edmund Chong
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