mvpa.clfs.libsvmc.sens

Provide sensitivity measures for libsvm’s SVM.

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

LinearSVMWeights

class mvpa.clfs.libsvmc.sens.LinearSVMWeights(clf, **kwargs)

Bases: mvpa.measures.base.Sensitivity

SensitivityAnalyzer for the LIBSVM implementation of a linear SVM.

Note

Available state variables:

• base_sensitivities: Stores basic sensitivities if the sensitivity relies on combining multiple ones
• biases+: Offsets of separating hyperplanes
• null_prob+: State variable
• null_t: State variable
• raw_result: 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:

Sensitivity

Initialize the analyzer with the classifier it shall use.

Parameters:

• clf (LinearSVM) – classifier to use. Only classifiers sub-classed from LinearSVM may be used.
• 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
• force_training (Bool) – if classifier was already trained – do not retrain
• combiner (Functor) – The combiner is only applied if the computed featurewise dataset measure is more than one-dimensional. This is different from a transformer, which is always applied. By default, the sum of absolute values along the second axis is computed.

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 LinearSVMWeights documentation.

Full API documentation of LinearSVMWeights in module mvpa.clfs.libsvmc.sens.