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Starts with Expectation step. Initial values of the model parameters will be estimated by the k-means algorithm.

Namespace: Emgu.CV.ML
Assembly: Emgu.CV.ML (in Emgu.CV.ML.dll) Version: 2.4.2.1777 (2.4.2.1777)

Syntax

C#
public bool Train(
	Matrix<float> samples,
	Matrix<int> labels,
	Matrix<float> probs,
	Matrix<double> logLikelihoods
)
Visual Basic
Public Function Train ( _
	samples As Matrix(Of Single), _
	labels As Matrix(Of Integer), _
	probs As Matrix(Of Single), _
	logLikelihoods As Matrix(Of Double) _
) As Boolean
Visual C++
public:
bool Train(
	Matrix<float>^ samples, 
	Matrix<int>^ labels, 
	Matrix<float>^ probs, 
	Matrix<double>^ logLikelihoods
)

Parameters

samples
Type: Emgu.CV..::..Matrix<(Of <(<'Single>)>)>
The training data. A 32-bit floating-point, single-channel matrix, one vector per row
labels
Type: Emgu.CV..::..Matrix<(Of <(<'Int32>)>)>
Can be null if not needed. Optionally computed output "class label" for each sample
probs
Type: Emgu.CV..::..Matrix<(Of <(<'Single>)>)>
Can be null if not needed. Initial probabilities p_{i,k} of sample i to belong to mixture component k. It is a one-channel floating-point matrix of nsamples x nclusters size.
logLikelihoods
Type: Emgu.CV..::..Matrix<(Of <(<'Double>)>)>
Can be null if not needed. The optional output matrix that contains a likelihood logarithm value for each sample. It has nsamples x 1 size and CV_64FC1 type.

Return Value

The methods return true if the Gaussian mixture model was trained successfully, otherwise it returns false.

See Also