| 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.svd

## Module: `mappers.svd`

Inheritance diagram for `mvpa.mappers.svd`:

Singular-value decomposition mapper

## `SVDMapper`

class `mvpa.mappers.svd.``SVDMapper`(**kwargs)

  Bases: `mvpa.mappers.base.ProjectionMapper`

  Mapper to project data onto SVD components estimated from some dataset.

  See also

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

  `ProjectionMapper`

  Initialize the SVDMapper

  Parameters:

  - 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()

  Note, that for the ‘selector’ argument this class also supports passing a ElementSelector instance, which will be used to determine the to be selected features, based on the singular values of each component.

  `selectOut`(outIds)

    Choose a subset of SVD components (and remove all others).

  `sv`

    Singular values

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