mvpa2.mappers.mdp_adaptor.ICAMapper

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

Convenience wrapper to perform ICA using MDP nodes.

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.
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

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 : {‘FastICA’, ‘CuBICA’}

Which MDP implementation of an ICA to use.

nodeargs : None or dict

Arguments passed to the MDP node in various stages of its lifetime. See the baseclass 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.
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

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
proj

Projection matrix (as an array)

recon

Backprojection matrix (as an array)