EMTrainE Method

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

Public Overridable Function TrainE ( 
	samples As InputArray,
	means0 As InputArray,
	Optional covs0 As InputArray = Nothing,
	Optional weights0 As InputArray = Nothing,
	Optional logLikelihoods As OutputArray = Nothing,
	Optional labels As OutputArray = Nothing,
	Optional probs As OutputArray = Nothing
) As Boolean

public:
virtual bool TrainE(
	InputArray^ samples, 
	InputArray^ means0, 
	InputArray^ covs0 = nullptr, 
	InputArray^ weights0 = nullptr, 
	OutputArray^ logLikelihoods = nullptr, 
	OutputArray^ labels = nullptr, 
	OutputArray^ probs = nullptr
)

abstract TrainE : 
        samples : InputArray * 
        means0 : InputArray * 
        ?covs0 : InputArray * 
        ?weights0 : InputArray * 
        ?logLikelihoods : OutputArray * 
        ?labels : OutputArray * 
        ?probs : OutputArray 
(* Defaults:
        let _covs0 = defaultArg covs0 null
        let _weights0 = defaultArg weights0 null
        let _logLikelihoods = defaultArg logLikelihoods null
        let _labels = defaultArg labels null
        let _probs = defaultArg probs null
*)
-> bool 
override TrainE : 
        samples : InputArray * 
        means0 : InputArray * 
        ?covs0 : InputArray * 
        ?weights0 : InputArray * 
        ?logLikelihoods : OutputArray * 
        ?labels : OutputArray * 
        ?probs : OutputArray 
(* Defaults:
        let _covs0 = defaultArg covs0 null
        let _weights0 = defaultArg weights0 null
        let _logLikelihoods = defaultArg logLikelihoods null
        let _labels = defaultArg labels null
        let _probs = defaultArg probs null
*)
-> bool 

Parameters

samples

Type: OpenCvSharpInputArray
Samples from which the Gaussian mixture model will be estimated. It should be a one-channel matrix, each row of which is a sample. If the matrix does not have CV_64F type it will be converted to the inner matrix of such type for the further computing.

means0

Type: OpenCvSharpInputArray
Initial means \f$a_k\f$ of mixture components. It is a one-channel matrix of \f$nclusters \times dims\f$ size. If the matrix does not have CV_64F type it will be converted to the inner matrix of such type for the further computing.

covs0 (Optional)

Type: OpenCvSharpInputArray
The vector of initial covariance matrices \f$S_k\f$ of mixture components. Each of covariance matrices is a one-channel matrix of \f$dims \times dims\f$ size. If the matrices do not have CV_64F type they will be converted to the inner matrices of such type for the further computing.

weights0 (Optional)

Type: OpenCvSharpInputArray
Initial weights \f$\pi_k\f$ of mixture components. It should be a one-channel floating-point matrix with \f$1 \times nclusters\f$ or \f$nclusters \times 1\f$ size.

logLikelihoods (Optional)

Type: OpenCvSharpOutputArray
The optional output matrix that contains a likelihood logarithm value for each sample. It has \f$nsamples \times 1\f$ size and CV_64FC1 type.

labels (Optional)

Type: OpenCvSharpOutputArray
The optional output "class label" for each sample: \f$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\f$ (indices of the most probable mixture component for each sample). It has \f$nsamples \times 1\f$ size and CV_32SC1 type.

probs (Optional)

Type: OpenCvSharpOutputArray
The optional output matrix that contains posterior probabilities of each Gaussian mixture component given the each sample. It has \f$nsamples \times nclusters\f$ size and CV_64FC1 type.

Return Value

[Missing <returns> documentation for "M:OpenCvSharp.EM.TrainE(OpenCvSharp.InputArray,OpenCvSharp.InputArray,OpenCvSharp.InputArray,OpenCvSharp.InputArray,OpenCvSharp.OutputArray,OpenCvSharp.OutputArray,OpenCvSharp.OutputArray)"]

Reference