Package measures

PyMVPA measures.

Module Description

Provide some measures given a dataset. Most of the time, derivatives of FeaturewiseDatasetMeasure are used, such as

Also many classifiers natively provide sensitivity estimators via the call to getSensitivityAnalyzer method

Submodules

mvpa.measures.anova: FeaturewiseDatasetMeasure performing a univariate ANOVA.
mvpa.measures.base: Base class for data measures: algorithms that quantify properties of datasets.
mvpa.measures.corrcoef: FeaturewiseDatasetMeasure of correlation with the labels.
mvpa.measures.corrstability: FeaturewiseDatasetMeasure of stability of labels across chunks based on correlation.
mvpa.measures.ds: Dissimilarity measure.
mvpa.measures.glm: The general linear model (GLM).
mvpa.measures.irelief: FeaturewiseDatasetMeasure performing multivariate Iterative RELIEF (I-RELIEF) algorithm. See : Y. Sun, Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications, IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), vol. 29, no. 6, pp. 1035-1051, June 2007.
mvpa.measures.noiseperturbation: This is a FeaturewiseDatasetMeasure that uses a scalar DatasetMeasure and selective noise perturbation to compute a sensitivity map.
mvpa.measures.pls
mvpa.measures.searchlight: Implementation of the Searchlight algorithm
mvpa.measures.splitmeasure: This is a FeaturewiseDatasetMeasure that uses another FeaturewiseDatasetMeasure and runs it multiple times on differents splits of a Dataset.

Imports: debug

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