| Multivariate Pattern Analysis in Python

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This content refers to the previous stable release of PyMVPA. Please visit www.pymvpa.org for the most recent version of PyMVPA and its documentation.

# mappers.procrustean

## Module: `mappers.procrustean`

Inheritance diagram for `mvpa.mappers.procrustean`:

Procrustean rotation mapper

## `ProcrusteanMapper`

class `mvpa.mappers.procrustean.``ProcrusteanMapper`(scaling=True, reflection=True, reduction=True, oblique=False, oblique_rcond=-1, **kwargs)

  Bases: `mvpa.mappers.base.ProjectionMapper`

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

  See also

  Please refer to the documentation of the base class for more information:

  `ProjectionMapper`

  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.
  - selector (None | list) – Which components (i.e. columns of the projection matrix) should be used for mapping. If selector is None all components are used. If a list is provided, all list elements are treated as component ids and the respective components are selected (all others are discarded).
  - demean (bool) – Either data should be demeaned while computing projections and applied back while doing reverse()

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