mvpa2.algorithms.benchmarks.hyperalignment.timesegments_classification(dss, hyper=None, part1=HalfPartitioner(), part2=NFoldPartitioner(attr='subjects'), window_size=6, overlapping_windows=True, distance='correlation', do_zscore=True)

Time-segment classification across subjects using Hyperalignment


dss : list of datasets

Datasets to benchmark on. Usually a single dataset per subject.

hyper : Hyperalignment-like, optional

Beast which if called on a list of datasets should spit out trained mappers. If not specified, `IdentityMapper`s will be used

part1 : Partitioner, optional

Partitioner to split data for hyperalignment “cross-validation”

part2 : Partitioner, optional

Partitioner for CV within the hyperalignment test split

window_size : int, optional

How many temporal points to consider for a classification sample

overlapping_windows : bool, optional

Strategy to how create and classify “samples” for classification. If True – window_size samples from each time point (but trailing ones) constitute a sample, and upon “predict” window_size of samples around each test point is not considered. If False – samples are just taken (with training and testing splits) at window_size step from one to another.

do_zscore : bool, optional

Perform zscoring (overall, not per-chunk) for each dataset upon partitioning with part1