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Package mvpa :: Package clfs :: Module meta :: Class MaximalVote

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Class MaximalVote

Provides a decision using maximal vote rule

Nested Classes

Inherited from misc.state.ClassWithCollections: __metaclass__

Instance Methods

__init__(self)
XXX Might get a parameter to use raw decision values if voting is not unambigous (ie two classes have equal number of votes

source code

__call__(self, clfs, dataset)
Actuall callable - perform voting

source code

Inherited from PredictionsCombiner: train

Inherited from misc.state.ClassWithCollections: __getattribute__, __new__, __repr__, __setattr__, __str__, reset

Inherited from object: __delattr__, __format__, __hash__, __reduce__, __reduce_ex__, __sizeof__, __subclasshook__

Class Variables

predictions = StateVariable(enabled= True, doc= "Voted predict...

all_label_counts = StateVariable(enabled= False, doc= "Counts ...

Inherited from misc.state.ClassWithCollections: _DEV__doc__, descr

Properties

Inherited from object: __class__

Method Details

init(self)
(Constructor)

XXX Might get a parameter to use raw decision values if voting is not unambigous (ie two classes have equal number of votes

Overrides: object.__init__

call(self, clfs, dataset)
(Call operator)

Actuall callable - perform voting

Extended functionality which might not be needed actually: Since BinaryClassifier might return a list of possible predictions (not just a single one), we should consider all of those

MaximalVote doesn't care about dataset itself

Parameters:

clfs - List of classifiers to combine. Has to be classifiers (not pure predictions), since combiner might use some other state variables (value's) instead of pure prediction's

Overrides: PredictionsCombiner.__call__

Class Variable Details

predictions

Value:

StateVariable(enabled= True, doc= "Voted predictions")

all_label_counts

Value:

StateVariable(enabled= False, doc= "Counts across classifiers for each
 label/sample")

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