mvpa2.algorithms.hyperalignment.ProcrusteanMapper

Inheritance diagram of ProcrusteanMapper

class mvpa2.algorithms.hyperalignment.ProcrusteanMapper(space='targets', **kwargs)

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

Training this mapper requires data for both source and target space to be present in the training dataset. The source space data is taken from the training dataset’s samples, while the target space is taken from a sample attribute corresponding to the space setting of the ProcrusteanMapper.

See: http://en.wikipedia.org/wiki/Procrustes_transformation

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 +)

Methods

forward(data) Map data from input to output space.
forward1(data) Wrapper method to map single samples.
generate(ds) Yield processing results.
get_postproc() Returns the post-processing node or None.
get_space() Query the processing space name of this node.
reset()
reverse(data) Reverse-map data from output back into input space.
reverse1(data) Wrapper method to map single samples.
set_postproc(node) Assigns a post-processing node Set to None to disable postprocessing.
set_space(name) Set the processing space name of this node.
train(ds) The default implementation calls _pretrain(), _train(), and finally _posttrain().
untrain() Reverts changes in the state of this node caused by previous training

Initialize instance of ProcrusteanMapper

Parameters:

scaling :

Estimate a global scaling factor for the transformation (no longer rigid body). (Default: True)

reflection :

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

reduction :

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. (Default: True)

oblique :

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

oblique_rcond :

Cutoff for ‘small’ singular values to regularize the inverse. See lstsq for more information. (Default: -1)

svd :

Implementation of SVD to use. dgesvd requires ctypes to be available. (Default: ‘numpy’)

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()

auto_train : bool

Flag whether the learner will automatically train itself on the input dataset when called untrained.

force_train : bool

Flag whether the learner will enforce training on the input dataset upon every call.

space: str, optional :

Name of the ‘processing space’. The actual meaning of this argument heavily depends on the sub-class implementation. In general, this is a trigger that tells the node to compute and store information about the input data that is “interesting” in the context of the corresponding processing in the output dataset.

postproc : Node instance, optional

Node to perform post-processing of results. This node is applied in __call__() to perform a final processing step on the to be result dataset. If None, nothing is done.

descr : str

Description of the instance

Methods

forward(data) Map data from input to output space.
forward1(data) Wrapper method to map single samples.
generate(ds) Yield processing results.
get_postproc() Returns the post-processing node or None.
get_space() Query the processing space name of this node.
reset()
reverse(data) Reverse-map data from output back into input space.
reverse1(data) Wrapper method to map single samples.
set_postproc(node) Assigns a post-processing node Set to None to disable postprocessing.
set_space(name) Set the processing space name of this node.
train(ds) The default implementation calls _pretrain(), _train(), and finally _posttrain().
untrain() Reverts changes in the state of this node caused by previous training

NeuroDebian

NITRC-listed