| 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.zscore

## Module: `mappers.zscore`

Inheritance diagram for `mvpa.mappers.zscore`:

Simple mapper to perform zscoring

## `ZScoreMapper`

class `mvpa.mappers.zscore.``ZScoreMapper`(baselinelabels=None, **kwargs)

  Bases: `mvpa.mappers.base.ProjectionMapper`

  Mapper to project data into standardized values (z-scores).

  After the mapper has been instantiated, it has to be train first.

  Since it tries to reuse ProjectionMapper, invariant features will simply be assigned a std == 1, which would be equivalent to not standardizing them at all. This is similar to not touching them at all, so similar to what zscore function currently does

  See also

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

  `ProjectionMapper`

  Initialize ZScoreMapper

  Parameters:

  - baselinelabels (None or list of int or string) – Used to compute mean and variance TODO: not in effect now and need to be adherent to all `ProjectionMapper`s
  - 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()

### Navigation

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