C# Class OpenCvSharp.EM

The class implements the Expectation Maximization algorithm.

Inheritance: Algorithm

Afficher le fichier Open project: shimat/opencvsharp

Méthodes publiques

Méthode	Description
Create ( ) : EM	Creates empty EM model.
GetCovs ( ) : OpenCvSharp.Mat[]	Returns covariation matrices. Returns vector of covariation matrices. Number of matrices is the number of gaussian mixtures, each matrix is a square floating-point matrix NxN, where N is the space dimensionality.
GetMeans ( ) : OpenCvSharp.Mat	Returns the cluster centers (means of the Gaussian mixture). Returns matrix with the number of rows equal to the number of mixtures and number of columns equal to the space dimensionality.
GetWeights ( ) : OpenCvSharp.Mat	Returns weights of the mixtures. Returns vector with the number of elements equal to the number of mixtures.
Predict2 ( InputArray sample, OutputArray probs = null ) : Vec2d	Predicts the response for sample
TrainE ( InputArray samples, InputArray means0, InputArray covs0 = null, InputArray weights0 = null, OutputArray logLikelihoods = null, OutputArray labels = null, OutputArray probs = null ) : bool	Estimates Gaussian mixture parameters from the sample set
TrainM ( InputArray samples, InputArray probs0, OutputArray logLikelihoods = null, OutputArray labels = null, OutputArray probs = null ) : bool	Estimates Gaussian mixture parameters from the sample set

Méthodes protégées

Méthode	Description
Dispose ( bool disposing ) : void	Clean up any resources being used.
EM ( IntPtr p ) : System	Creates instance by raw pointer cv::ml::EM*

Method Details

Create() public static méthode

Creates empty EM model.

public static Create ( ) : EM
Résultat	EM

Dispose() protected méthode

Clean up any resources being used.

protected Dispose ( bool disposing ) : void
disposing	bool	/// If disposing equals true, the method has been called directly or indirectly by a user's code. Managed and unmanaged resources can be disposed. /// If false, the method has been called by the runtime from inside the finalizer and you should not reference other objects. Only unmanaged resources can be disposed. ///
Résultat	void

EM() protected méthode

Creates instance by raw pointer cv::ml::EM*

protected EM ( IntPtr p ) : System
p	System.IntPtr
Résultat	System

GetCovs() public méthode

Returns covariation matrices. Returns vector of covariation matrices. Number of matrices is the number of gaussian mixtures, each matrix is a square floating-point matrix NxN, where N is the space dimensionality.

public GetCovs ( ) : OpenCvSharp.Mat[]
Résultat	OpenCvSharp.Mat[]

GetMeans() public méthode

Returns the cluster centers (means of the Gaussian mixture). Returns matrix with the number of rows equal to the number of mixtures and number of columns equal to the space dimensionality.

public GetMeans ( ) : OpenCvSharp.Mat
Résultat	OpenCvSharp.Mat

GetWeights() public méthode

Returns weights of the mixtures. Returns vector with the number of elements equal to the number of mixtures.

public GetWeights ( ) : OpenCvSharp.Mat
Résultat	OpenCvSharp.Mat

Predict2() public méthode

Predicts the response for sample

public Predict2 ( InputArray sample, OutputArray probs = null ) : Vec2d
sample	InputArray
probs	OutputArray
Résultat	Vec2d

TrainE() public méthode

Estimates Gaussian mixture parameters from the sample set

public TrainE ( InputArray samples, InputArray means0, InputArray covs0 = null, InputArray weights0 = null, OutputArray logLikelihoods = null, OutputArray labels = null, OutputArray probs = null ) : bool
samples	InputArray
means0	InputArray
covs0	InputArray
weights0	InputArray
logLikelihoods	OutputArray
labels	OutputArray
probs	OutputArray
Résultat	bool

TrainM() public méthode

Estimates Gaussian mixture parameters from the sample set

public TrainM ( InputArray samples, InputArray probs0, OutputArray logLikelihoods = null, OutputArray labels = null, OutputArray probs = null ) : bool
samples	InputArray
probs0	InputArray
logLikelihoods	OutputArray
labels	OutputArray
probs	OutputArray
Résultat	bool