| Multivariate Pattern Analysis in Python

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This content refers to the previous stable release of PyMVPA. Please visit www.pymvpa.org for the most recent version of PyMVPA and its documentation.

# measures.noiseperturbation

## Module: `measures.noiseperturbation`

Inheritance diagram for `mvpa.measures.noiseperturbation`:

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

## `NoisePerturbationSensitivity`

class `mvpa.measures.noiseperturbation.``NoisePerturbationSensitivity`(datameasure, noise=<built-in method normal of mtrand.RandomState object at 0x557e8930>)

  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!

  Note

  Available state variables:

  - base_sensitivities: Stores basic sensitivities if the sensitivity relies on combining multiple ones
  - null_prob+: State variable
  - null_t: State variable
  - raw_results: Computed results before applying any transformation algorithm

  (States enabled by default are listed with +)

  See also

  Please refer to the documentation of the base class for more information:

  `FeaturewiseDatasetMeasure`

  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.
  - 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

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