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Multivariate Pattern Analysis in Python |
Implementation of the Searchlight algorithm
The comprehensive API documentation for this module, including all technical details, is available in the Epydoc-generated API reference for mvpa.measures.searchlight (for developers).
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).
Parameters: |
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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.
See also
Derived classes might provide additional methods via their base classes. Please refer to the list of base classes (if it exists) at the begining of the Searchlight documentation.
Full API documentation of Searchlight in module mvpa.measures.searchlight.