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EMTrainM Method
http://www.emgu.com
Estimate the Gaussian mixture parameters from a samples set. This variation starts with Expectation step. Initial values of the model parameters will be estimated by the k-means algorithm. Unlike many of the ML models, EM is an unsupervised learning algorithm and it does not take responses (class labels or function values) as input. Instead, it computes the Maximum Likelihood Estimate of the Gaussian mixture parameters from an input sample set, stores all the parameters inside the structure, and optionally computes the output "class label" for each sample. The trained model can be used further for prediction, just like any other classifier.

Namespace:  Emgu.CV.ML
Assembly:  Emgu.CV.World (in Emgu.CV.World.dll) Version: 3.3.0.2824 (3.3.0.2824)
Syntax
public void TrainM(
	IInputArray samples,
	IInputArray probs0,
	IOutputArray logLikelihoods = null,
	IOutputArray labels = null,
	IOutputArray probs = null
)

Parameters

samples
Type: Emgu.CVIInputArray
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: Emgu.CVIInputArray
The probs0.
logLikelihoods (Optional)
Type: Emgu.CVIOutputArray
The optional output matrix that contains a likelihood logarithm value for each sample. It has nsamples x 1 size and CV_64FC1 type.
labels (Optional)
Type: Emgu.CVIOutputArray
The optional output "class label" for each sample(indices of the most probable mixture component for each sample). It has nsamples x 1 size and CV_32SC1 type.
probs (Optional)
Type: Emgu.CVIOutputArray
The optional output matrix that contains posterior probabilities of each Gaussian mixture component given the each sample. It has nsamples x nclusters size and CV_64FC1 type.
See Also