|  | EMTrainEM Method  | 
 
            Estimate the Gaussian mixture parameters from a samples set.
            
 
    Namespace: 
   OpenCvSharp
    Assembly:
   OpenCvSharp (in OpenCvSharp.dll) Version: 1.0.0
 Syntax
Syntaxpublic virtual bool TrainEM(
	InputArray samples,
	OutputArray logLikelihoods = null,
	OutputArray labels = null,
	OutputArray probs = null
)
Public Overridable Function TrainEM ( 
	samples As InputArray,
	Optional logLikelihoods As OutputArray = Nothing,
	Optional labels As OutputArray = Nothing,
	Optional probs As OutputArray = Nothing
) As Boolean
public:
virtual bool TrainEM(
	InputArray^ samples, 
	OutputArray^ logLikelihoods = nullptr, 
	OutputArray^ labels = nullptr, 
	OutputArray^ probs = nullptr
)
abstract TrainEM : 
        samples : InputArray * 
        ?logLikelihoods : OutputArray * 
        ?labels : OutputArray * 
        ?probs : OutputArray 
(* Defaults:
        let _logLikelihoods = defaultArg logLikelihoods null
        let _labels = defaultArg labels null
        let _probs = defaultArg probs null
*)
-> bool 
override TrainEM : 
        samples : InputArray * 
        ?logLikelihoods : OutputArray * 
        ?labels : OutputArray * 
        ?probs : OutputArray 
(* Defaults:
        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.
- 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
Type: 
Boolean[Missing <returns> documentation for "M:OpenCvSharp.EM.TrainEM(OpenCvSharp.InputArray,OpenCvSharp.OutputArray,OpenCvSharp.OutputArray,OpenCvSharp.OutputArray)"]
 See Also
See Also