ENH: allow MulticlassClassifier to operate without combiner thus just spitting out all pair-wise predictions (without any voting)

work here is to superseed even more ad-hoc and fragile approach now
found under mvpa2/sandbox/multiclass
RF: Removing what was apparently copied/fixed in sandbox/multiclass.py
RF: no "combiner" -- no "estimates" in CombinedClassifier (and no warning)
RF: squeeze out the degenerate dimensions for pos/neg labels in pair-wise classifiers
RF: place labels for pair-wise results into a single fa matching original attribute (e.g. "targets") in tuples

our sensitivity analyzers already do the same
Tuples allow to have varying number of labels for each member, e.g.
if it was 1-vs-all
BF: if no combiner -- place training_stats into a dict

this is probably more of a patch than a proper solution, but since I do not expect people
jump on using it, may be later we could figure out a better one during RF of all the
summary stats constructs
Merge branch '_tent/pairwise_clf_performances' of https://github.com/yarikoptic/PyMVPA

* '_tent/pairwise_clf_performances' of https://github.com/yarikoptic/PyMVPA:
  BF: if no combiner -- place training_stats into a dict
  RF: place labels for pair-wise results into a single fa matching original attribute (e.g. "targets") in tuples
  RF: squeeze out the degenerate dimensions for pos/neg labels in pair-wise classifiers
  RF: no "combiner" -- no "estimates" in CombinedClassifier (and no warning)
  RF: Removing what was apparently copied/fixed in sandbox/multiclass.py
  ENH: allow MulticlassClassifier to operate without combiner thus just spitting out all pair-wise predictions (without any voting)

Conflicts:
	mvpa2/clfs/base.py -- used convenience ca instead of self.ca