RF: SimpleStatBaseSearchlight to absorb common logic for ad-hoc searchlights

SimpleStatBaseSearchlight takes care about collecting base statistics for all the
samples in targets/chunks groups and then passes all that into a derived class
_sl_call_on_a_split  for every split so it could make a classification decision
(and compute an accuracy -- which should go upstairs I guess)
RF: moved computing errors up into the SimpleStatBaseSearchlight
RF: minor chanes in test_searchlight in preparation for mnnsearchlight test
RF: variables renamings in gnb/adhoc searchlight to ease code understanding etc
BF: assure operating on floats while collecting statistics

otherwise such operations as np.power(X) could overflow on int
resulting in negative variances

detected while running on non-zscored haxby2001 dataset
OPT: minor optimization -- avoid product + square - use single product
RF: further rf the unittest to prep for nnsearchlight
RF: adhoc searchlights -- moving functionality from a lengthy _call into methods
RF: group stats within a dummy object, remove local bindings (of no help really)
ENH: make mvpa2.seed() to reseed with internal seed by default
ENH: expose kNN.dfx
NF: K1NNSearchlight - basic implementation which seems to pass the tests
ENH: test GNB and M1NN SLs on a multiclass case
ENH: expose nnsearchlights in suite
ENH: extended Sifter with ability to discard unbalanced partitions
ENH: option reuse_neighbors for adhoc searchlights -- to allow efficient perm testing
RF: AttributePermutator - got __repr__, nruns deprecated in favor of count to match __init__ signature
BF: # of rois corresponds to the 2nd dim in the sparse matrix
ENH: smoke test for mc null estimate on m1nnsearchlight
Import multi-class Bayes factor code