mvpa2.mappers.mdp_adaptor.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+
: Noneraw_results
: Nonetrained_dataset
: Nonetrained_nsamples+
: Nonetrained_targets+
: Nonetraining_time+
: None
(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 everycalled. 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
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 everycalled. 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
-
centroid
¶ Mean of the training data
-
proj
¶ Projection matrix (as an array)
-
recon
¶ Backprojection matrix (as an array)
-
var
¶ Variances per component