Data aggregation procedures

Inheritance diagram of mvpa2.measures.winner


feature_loser_measure() takes loser over features
feature_winner_measure() takes winner over features
group_sample_loser_measure([attrs]) takes loser after meaning over attrs
group_sample_winner_measure([attrs]) takes winner after meaning over attrs
mean_group_sample(attrs[, attrfx]) Returns a mapper that computes the mean samples of unique sample groups.
sample_loser_measure() takes loser over samples
sample_winner_measure() takes winner over samples
vstack(datasets[, a, fa]) Stacks datasets vertically (appending samples).


ChainLearner(learners[, auto_train, force_train]) Combines different learners into one in a chained fashion
ChainNode(nodes, \*\*kwargs) This class allows to concatenate a list of nodes into a processing chain.
Dataset(samples[, sa, fa, a]) Generic storage class for datasets with multiple attributes.
Measure([null_dist]) A measure computed from a Dataset
WinnerMeasure(axis, fx[, other_axis_prefix]) Select a “winning” element along samples or features.
partial partial(func, *args, **keywords) - new function with partial application