Multivariate Pattern Analysis in Python

# mvpa2.algorithms.benchmarks.hyperalignment.timesegments_classification

`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

  Parameters:

  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

  …