mvpa2.measures.winnerΒΆ
Data aggregation procedures
Functions
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). |
Classes
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 |
