mvpa2.mappers.mdp_adaptor.PCAMapper

Inheritance diagram of PCAMapper
class mvpa2.mappers.mdp_adaptor.PCAMapper(alg='PCA', nodeargs=None, **kwargs)

Convenience wrapper to perform PCA using MDP’s Mapper

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.
  • trained_dataset: The dataset it has been trained on
  • trained_nsamples+: Number of samples it has been trained on
  • trained_targets+: Set of unique targets (or any other space) it has been trained on (if present in the dataset trained on)
  • training_time+: Time (in seconds) it took to train the learner

(Conditional attributes enabled by default suffixed with +)

Attributes

auto_train Whether the Learner performs automatic trainingwhen called untrained.
centroid Mean of the training data
descr Description of the object if any
force_train Whether the Learner enforces training upon every call.
is_trained Whether the Learner is currently trained.
pass_attr Which attributes of the dataset or self.ca to pass into result dataset upon call
postproc Node to perform post-processing of results
proj Projection matrix (as an array)
recon Backprojection matrix (as an array)
space Processing space name of this node
var Variances per component

Methods

__call__(ds)
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_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
Parameters:

alg : {‘PCA’, ‘NIPALS’}

Which MDP implementation of a PCA to use.

nodeargs : None or dict

Arguments passed to the MDP node in various stages of its lifetime. See the MDPNodeMapper for more details.

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

node : mdp.Node instance

This node instance is taken as the pristine source of which a copy is made for actual processing upon each training attempt.

Attributes

auto_train Whether the Learner performs automatic trainingwhen called untrained.
centroid Mean of the training data
descr Description of the object if any
force_train Whether the Learner enforces training upon every call.
is_trained Whether the Learner is currently trained.
pass_attr Which attributes of the dataset or self.ca to pass into result dataset upon call
postproc Node to perform post-processing of results
proj Projection matrix (as an array)
recon Backprojection matrix (as an array)
space Processing space name of this node
var Variances per component

Methods

__call__(ds)
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_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
centroid

Mean of the training data

proj

Projection matrix (as an array)

recon

Backprojection matrix (as an array)

var

Variances per component