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clfs.sg.sens
Module: clfs.sg.sens
Inheritance diagram for mvpa.clfs.sg.sens:
Provide sensitivity measures for sg’s SVM.
-
class mvpa.clfs.sg.sens.LinearSVMWeights(clf, **kwargs)
Bases: mvpa.measures.base.Sensitivity
Sensitivity that reports the weights of a linear SVM trained
on a given Dataset.
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_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:
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.
- transformer (Functor) – This functor is called in __call__() to perform a final
processing step on the to be returned dataset measure. If None,
nothing is called
- null_dist (instance of distribution estimator) – The estimated distribution is used to assign a probability for a
certain value of the computed measure.
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