| 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.

# algorithms.hyperalignment

## Module: `algorithms.hyperalignment`

Inheritance diagram for `mvpa.algorithms.hyperalignment`:

Hyperalignment of functional data to the common space

References: TODO...

see SMLR code for example on how to embed the reference so in future it gets properly referenced...

## `Hyperalignment`

class `mvpa.algorithms.hyperalignment.``Hyperalignment`(alignment=None, levels=3, combiner1='mean', combiner2='mean', **kwargs)

  Bases: `mvpa.misc.state.ClassWithCollections`

  ...

  > Given a set of datasets (may be just data) provide mapping of features into a common space

  Note

  Available state variables:

  - who_knows_maybe_something_to_store_optionally: ....

  (States enabled by default are listed with +)

  See also

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

  `ClassWithCollections`

  Initialize instance of Hyperalignment

  Parameters:

  - alignment – ... XXX If None (default) an instance of `ProcrusteanMapper` is used. (Default: None)
  - levels – Number of levels ....XXX . (Default: 3)
  - combiner1 – XXX . (Default: mean)
  - combiner2 – XXX . (Default: mean)
  - enable_states (None or list of basestring) – Names of the state variables which should be enabled additionally to default ones
  - disable_states (None or list of basestring) – Names of the state variables which should be disabled

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