http://www.emgu.com
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: 2.4.0.1717 (2.4.0.1717)

Syntax

C#
public static bool CvEMTrain(
	IntPtr model,
	IntPtr samples,
	IntPtr labels,
	IntPtr probs,
	IntPtr logLikelihoods
)
Visual Basic
Public Shared Function CvEMTrain ( _
	model As IntPtr, _
	samples As IntPtr, _
	labels As IntPtr, _
	probs As IntPtr, _
	logLikelihoods As IntPtr _
) As Boolean
Visual C++
public:
static bool CvEMTrain(
	IntPtr model, 
	IntPtr samples, 
	IntPtr labels, 
	IntPtr probs, 
	IntPtr logLikelihoods
)

Parameters

model
Type: System..::..IntPtr
The EM model
samples
Type: System..::..IntPtr
The training data. A 32-bit floating-point, single-channel matrix, one vector per row
labels
Type: System..::..IntPtr
Can be IntPtr.Zero if not needed. Optionally computed output "class label" for each sample
probs
Type: System..::..IntPtr
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: System..::..IntPtr
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