mvpa2.algorithms.hyperalignment.ProcrusteanMapper

Inheritance diagram of ProcrusteanMapper

class mvpa2.algorithms.hyperalignment.ProcrusteanMapper(scaling=True, reflection=True, reduction=True, oblique=False, oblique_rcond=-1, svd='numpy', **kwargs)

Mapper to project from one space to another using Procrustean transformation (shift + scaling + rotation)

Notes

Available conditional attributes:

  • calling_time+: Time (in seconds) it took to call the node
  • raw_results: Computed results before invoking postproc. Stored only if postproc is not None.
  • training_time+: Time (in seconds) it took to train the learner

(Conditional attributes enabled by default suffixed with +)

Initialize the ProcrusteanMapper

Parameters :

scaling : bool

Scale data for the transformation (no longer rigid body transformation)

reflection : bool

Allow for the data to be reflected (so it might not be a rotation). Effective only for non-oblique transformations

reduction : bool

If true, it is allowed to map into lower-dimensional space. Forward transformation might be suboptimal then and reverse transformation might not recover all original variance

oblique : bool

Either to allow non-orthogonal transformation – might heavily overfit the data if there is less samples than dimensions. Use oblique_rcond.

oblique_rcond : float

Cutoff for ‘small’ singular values to regularize the inverse. See lstsq for more information.

svd : string (numpy, scipy, dgesvd), optional

Implementation of SVD to use. dgesvd requires ctypes to be available.

enable_ca : None or list of str

Names of the conditional attributes which should be enabled in addition to the default ones

disable_ca : None or list of str

Names of the conditional attributes which should be disabled

demean : bool

Either data should be demeaned while computing projections and applied back while doing reverse()

NeuroDebian

NITRC-listed