[pymvpa] Sensitivity analysis with GNB

[Thomas Nickson]

I've trained a GNB classifier on a dataset with moderate success and would
like to look at the regions that the classifier finds most interesting. I
notice in the code that there is a sensitivity analyser for the GNB module
but that it has been removed:

# XXX Later come up with some  # could be a simple t-test maps using
distributions  # per each class  #def get_sensitivity_analyzer(self,
**kwargs):  # """Returns a sensitivity analyzer for GNB."""  # return
GNBWeights(self, **kwargs)

  # XXX Is there any reason to use properties?  #means = property(lambda
self: self.__biases)  #variances = property(lambda self: self.__weights)

## class GNBWeights(Sensitivity):  ## """`SensitivityAnalyzer` that reports
the weights GNB trained  ## on a given `Dataset`.
 ## """
 ## _LEGAL_CLFS = [ GNB ]
 ## def _call(self, dataset=None):  ## """Extract weights from GNB
classifier.
 ## GNB always has weights available, so nothing has to be computed here.  ##
"""  ## clf = self.clf  ## means = clf.means  ## XXX we can do something
better ;)  ## return mean
Is the use of the means considered to be poor in some sense?

Could anyone provide more information about this:

# XXX Later come up with some # could be a simple t-test maps using
distributions # per each class

Thanks,

Tom
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