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measures.searchlight
Module: measures.searchlight
Inheritance diagram for mvpa.measures.searchlight:
Implementation of the Searchlight algorithm
-
class mvpa.measures.searchlight.Searchlight(datameasure, radius=1.0, center_ids=None, **kwargs)
Bases: mvpa.measures.base.DatasetMeasure
Runs a scalar DatasetMeasure on all possible spheres of a certain size
within a dataset.
The idea for a searchlight algorithm stems from a paper by
Kriegeskorte et al. (2006).
See also
Please refer to the documentation of the base class for more information:
DatasetMeasure
Note
Available state variables:
- null_prob+: State variable
- null_t: State variable
- raw_results: Computed results before applying any transformation algorithm
- spheresizes: Number of features in each sphere.
(States enabled by default are listed with +)
See also
Please refer to the documentation of the base class for more information:
DatasetMeasure
Parameters: |
- datameasure (callable) – Any object that takes a Dataset
and returns some measure when called.
- radius (float) – All features within the radius around the center will be part
of a sphere. Provided dataset should have a metric assigned
(for NiftiDataset, voxel size is used to provide such a metric,
hence radius should be specified in mm).
- center_ids (list(int)) – List of feature ids (not coordinates) the shall serve as sphere
centers. By default all features will be used.
- transformer (Functor) – This functor is called in __call__() to perform a final
processing step on the to be returned dataset measure. If None,
nothing is called
- null_dist (instance of distribution estimator) – The estimated distribution is used to assign a probability for a
certain value of the computed measure.
- enable_states (None or list of basestring) – Names of the state variables which should be enabled additionally
to default ones
- disable_states (None or list of basestring) – Names of the state variables which should be disabled
- enable_states – Names of the state variables which should be enabled additionally
to default ones
- disable_states – Names of the state variables which should be disabled
|
Note
If Searchlight is used as SensitivityAnalyzer one has to make
sure that the specified scalar DatasetMeasure returns large
(absolute) values for high sensitivities and small (absolute) values
for low sensitivities. Especially when using error functions usually
low values imply high performance and therefore high sensitivity.
This would in turn result in sensitivity maps that have low
(absolute) values indicating high sensitivites and this conflicts
with the intended behavior of a SensitivityAnalyzer.