mvpa2.measures.corrstability.FeaturewiseMeasure¶
- class mvpa2.measures.corrstability.FeaturewiseMeasure(null_dist=None, **kwargs)¶
A per-feature-measure computed from a Dataset (base class).
Should behave like a Measure.
Notes
Available conditional attributes:
- calling_time+: Time (in seconds) it took to call the node
- null_prob+: None
- null_t: None
- 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 +)
Parameters : null_dist : instance of distribution estimator
The estimated distribution is used to assign a probability for a certain value of the computed measure.
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.
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
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
