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EMTrainM Method

Estimate the Gaussian mixture parameters from a samples set.

Namespace:  OpenCvSharp
Assembly:  OpenCvSharp (in OpenCvSharp.dll) Version: 1.0.0
Syntax
public virtual bool TrainM(
	InputArray samples,
	InputArray probs0,
	OutputArray logLikelihoods = null,
	OutputArray labels = null,
	OutputArray probs = null
)

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.
probs0
Type: OpenCvSharpInputArray
the probabilities
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.TrainM(OpenCvSharp.InputArray,OpenCvSharp.InputArray,OpenCvSharp.OutputArray,OpenCvSharp.OutputArray,OpenCvSharp.OutputArray)"]

See Also