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mvpa.clfs.smlr

Sparse Multinomial Logistic Regression classifier.

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

Classes

SMLR

class mvpa.clfs.smlr.SMLR(**kwargs)

Bases: mvpa.clfs.base.Classifier

Sparse Multinomial Logistic Regression Classifier.

This is an implementation of the SMLR algorithm published in Krishnapuram et al. (2005, IEEE Transactions on Pattern Analysis and Machine Intelligence). Be sure to cite that article if you use this for your work.

Initialize an SMLR classifier.

biases
getSensitivityAnalyzer(**kwargs)
Returns a sensitivity analyzer for SMLR.
weights

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 SMLR documentation.

Full API documentation of SMLR in module mvpa.clfs.smlr.

SMLRWeights

class mvpa.clfs.smlr.SMLRWeights(clf, force_training=True, **kwargs)

Bases: mvpa.measures.base.Sensitivity

SensitivityAnalyzer that reports the weights SMLR trained on a given Dataset.

By default SMLR provides multiple weights per feature (one per label in training dataset). By default, all weights are combined into a single sensitivity value. Please, see the FeaturewiseDatasetMeasure constructor arguments how to custmize this behavior.

Initialize the analyzer with the classifier it shall use.

Parameters:
  • clf (Classifier) – classifier to use.
  • force_training (Bool) – if classifier was already trained – do not retrain

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 SMLRWeights documentation.

Full API documentation of SMLRWeights in module mvpa.clfs.smlr.