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mvpa.measures.noiseperturbation

This is a FeaturewiseDatasetMeasure that uses a scalar DatasetMeasure and selective noise perturbation to compute a sensitivity map.

The comprehensive API documentation for this module, including all technical details, is available in the Epydoc-generated API reference for mvpa.measures.noiseperturbation (for developers).

NoisePerturbationSensitivity

class mvpa.measures.noiseperturbation.NoisePerturbationSensitivity(datameasure, noise=<built-in method normal of mtrand.RandomState object at 0x401ec350>)

Bases: mvpa.measures.base.FeaturewiseDatasetMeasure

This is a FeaturewiseDatasetMeasure that uses a scalar DatasetMeasure and selective noise perturbation to compute a sensitivity map.

First the scalar DatasetMeasure computed using the original dataset. Next the data measure is computed multiple times each with a single feature in the dataset perturbed by noise. The resulting difference in the scalar DatasetMeasure is used as the sensitivity for the respective perturbed feature. Large differences are treated as an indicator of a feature having great impact on the scalar DatasetMeasure.

The computed sensitivity map might have positive and negative values!

Cheap initialization.

Parameters
datameasure: Datameasure that is used to quantify the effect of
noise perturbation.
noise: Functor to generate noise. The noise generator has to return
an 1d array of n values when called the size=n keyword argument. This is the default interface of the random number generators in NumPy’s random module.

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 NoisePerturbationSensitivity documentation.

Full API documentation of NoisePerturbationSensitivity in module mvpa.measures.noiseperturbation.