C# Class MCAEmotiv.Classification.Classifiers

Extension methods for Classifiers

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Méthodes publiques

Méthode	Description
AccuracyOn ( this classifier, IEnumerable labeledExamples ) : double	Computes the classifier's accuracy in predicting the set of examples
AsOnlineClassifier ( this classifier ) : IOnlineClassifier	Wraps the classifier in an online classifier which works by storing all input examples and retraining the classifier on the stored examples as well as the new examples on each call to TrainMore
ErrorOn ( this classifier, IEnumerable labeledExamples ) : double	Computes the classifier's error in predicting the set of examples
IsNegative ( this classifier, Example example ) : bool	As `classifier.IsNegative(classifier, example.Class)`
IsNegative ( this classifier, int cls ) : bool	Does cls currently map to a negative example in this classifier?
IsPositive ( this classifier, Example example ) : bool	As `classifier.IsPositive(classifier, example.Class)`
IsPositive ( this classifier, int cls ) : bool	Does cls currently map to a positive example in this classifier?
Predict ( this classifier, IEnumerable examples ) : IEnumerable>	Returns a Trio of (example, predicted class, confidence) for each example
Predict ( this classifier, Example example ) : int	Predict without the confidence parameter
TrainMore ( this classifier, Example labeledExample ) : void	TrainMore, but with a single example

Method Details

AccuracyOn() public static méthode

Computes the classifier's accuracy in predicting the set of examples

public static AccuracyOn ( this classifier, IEnumerable labeledExamples ) : double
classifier	this
labeledExamples	IEnumerable
Résultat	double

AsOnlineClassifier() public static méthode

Wraps the classifier in an online classifier which works by storing all input examples and retraining the classifier on the stored examples as well as the new examples on each call to TrainMore

public static AsOnlineClassifier ( this classifier ) : IOnlineClassifier
classifier	this
Résultat	IOnlineClassifier

ErrorOn() public static méthode

Computes the classifier's error in predicting the set of examples

public static ErrorOn ( this classifier, IEnumerable labeledExamples ) : double
classifier	this
labeledExamples	IEnumerable
Résultat	double

IsNegative() public static méthode

As classifier.IsNegative(classifier, example.Class)

public static IsNegative ( this classifier, Example example ) : bool
classifier	this
example	Example
Résultat	bool

IsNegative() public static méthode

Does cls currently map to a negative example in this classifier?

public static IsNegative ( this classifier, int cls ) : bool
classifier	this
cls	int
Résultat	bool

IsPositive() public static méthode

As classifier.IsPositive(classifier, example.Class)

public static IsPositive ( this classifier, Example example ) : bool
classifier	this
example	Example
Résultat	bool

IsPositive() public static méthode

Does cls currently map to a positive example in this classifier?

public static IsPositive ( this classifier, int cls ) : bool
classifier	this
cls	int
Résultat	bool

Predict() public static méthode

Returns a Trio of (example, predicted class, confidence) for each example

public static Predict ( this classifier, IEnumerable examples ) : IEnumerable>
classifier	this
examples	IEnumerable
Résultat	IEnumerable>

Predict() public static méthode

Predict without the confidence parameter

public static Predict ( this classifier, Example example ) : int
classifier	this
example	Example
Résultat	int

TrainMore() public static méthode

TrainMore, but with a single example

public static TrainMore ( this classifier, Example labeledExample ) : void
classifier	this
labeledExample	Example
Résultat	void