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EM Class
Expectation Maximization model
Inheritance Hierarchy

Namespace:  Emgu.CV.ML
Assembly:  Emgu.CV.World (in Emgu.CV.World.dll) Version: (
public class EM : UnmanagedObject, IStatModel, IAlgorithm

The EM type exposes the following members.

Public methodEM
Create an Expectation Maximization model
Public propertyClustersNumber
The number of mixtures
Public propertyCovarianceMatrixType
The type of the mixture covariation matrices
Public propertyPtr
Pointer to the unmanaged object
(Inherited from UnmanagedObject.)
Public propertyTermCriteria
Termination criteria of the procedure. EM algorithm stops either after a certain number of iterations (term_crit.num_iter), or when the parameters change too little (no more than term_crit.epsilon) from iteration to iteration
Public methodDispose
The dispose function that implements IDisposable interface
(Inherited from DisposableObject.)
Protected methodDisposeObject
Release the memory associated with this EM model
(Overrides DisposableObjectDisposeObject.)
Public methodEquals (Inherited from Object.)
Protected methodFinalize
(Inherited from DisposableObject.)
Public methodGetHashCode (Inherited from Object.)
Public methodGetType (Inherited from Object.)
Protected methodMemberwiseClone (Inherited from Object.)
Public methodPredict
Predict the probability of the samples
Protected methodReleaseManagedResources
Release the managed resources. This function will be called during the disposal of the current object. override ride this function if you need to call the Dispose() function on any managed IDisposable object created by the current object
(Inherited from DisposableObject.)
Public methodToString (Inherited from Object.)
Public methodtrainE
Estimate the Gaussian mixture parameters from a samples set. This variation starts with Expectation step. You need to provide initial means of mixture components. Optionally you can pass initial weights and covariance matrices of mixture components.
Public methodTrainM
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.
Protected field_ptr
A pointer to the unmanaged object
(Inherited from UnmanagedObject.)
Extension Methods
Public Extension MethodClear
Clear the statistic model
(Defined by StatModelExtensions.)
Public Extension MethodPredict
Predicts response(s) for the provided sample(s)
(Defined by StatModelExtensions.)
Public Extension MethodRead
Reads algorithm parameters from a file storage.
(Defined by AlgorithmExtensions.)
Public Extension MethodSave
Save the statistic model to file
(Defined by StatModelExtensions.)
Public Extension MethodTrain(TrainData, Int32)Overloaded.
Trains the statistical model.
(Defined by StatModelExtensions.)
Public Extension MethodTrain(IInputArray, DataLayoutType, IInputArray)Overloaded.
Trains the statistical model.
(Defined by StatModelExtensions.)
Public Extension MethodWrite
Stores algorithm parameters in a file storage
(Defined by AlgorithmExtensions.)
See Also