there are 2 ways: 1. [available only in mvpa2] any RepeatedMeasure (including CrossValidation) takes argument 'callback':
callback : functor Optional callback to extract information from inside the main loop of the measure. The callback is called with the input 'data', the 'node' instance that is evaluated repeatedly and the 'result' of a single evaluation -- passed as named arguments (see labels in quotes) for every iteration, directly after evaluating the node. so there you could access anything you care about in the 'node', which is classifier in this case BUT because the same classifier instance gets reused through the iterations, you can't just "store" the classifier. you can deepcopy some of them (e.g. the ones relying on swig-ed APIs, like libsvm, would not be deepcopy-able) 2. SplitClassifier That one behaves similarly to cross-validation (just access its .ca.stats to get results of cross-validation), but also operates on copies of the originally provided classifier, so you could access all of them via .clfs attribute. Helps? On Sun, 08 Jan 2012, Tyson Aflalo wrote: > Is there a means of accessing each trained classifier that is generated as > part of a cross-validation analysis?� > Thanks, > tyson > _______________________________________________ > Pkg-ExpPsy-PyMVPA mailing list > Pkg-ExpPsy-PyMVPA@lists.alioth.debian.org > http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa -- =------------------------------------------------------------------= Keep in touch www.onerussian.com Yaroslav Halchenko www.ohloh.net/accounts/yarikoptic _______________________________________________ Pkg-ExpPsy-PyMVPA mailing list Pkg-ExpPsy-PyMVPA@lists.alioth.debian.org http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa