Class EM
- Namespace
- OpenCvSharp
- Assembly
- OpenCvSharp.dll
The class implements the Expectation Maximization algorithm.
public class EM : Algorithm, IDisposable- Inheritance
-
EM
- Implements
- Inherited Members
Fields
Properties
- ClustersNumber
The number of mixture components in the Gaussian mixture model. Default value of the parameter is EM::DEFAULT_NCLUSTERS=5. Some of EM implementation could determine the optimal number of mixtures within a specified value range, but that is not the case in ML yet.
- CovarianceMatrixType
Constraint on covariance matrices which defines type of matrices.
- TermCriteria
The termination criteria of the %EM algorithm. The EM algorithm can be terminated by the number of iterations termCrit.maxCount (number of M-steps) or when relative change of likelihood logarithm is less than termCrit.epsilon. Default maximum number of iterations is EM::DEFAULT_MAX_ITERS=100.
Methods
- Create()
Creates empty EM model.
- GetCovs()
Returns covariation matrices. Returns vector of covariation matrices. Number of matrices is the number of gaussian mixtures, each matrix is a square floating-point matrix NxN, where N is the space dimensionality.
- GetMeans()
Returns the cluster centers (means of the Gaussian mixture). Returns matrix with the number of rows equal to the number of mixtures and number of columns equal to the space dimensionality.
- GetWeights()
Returns weights of the mixtures. Returns vector with the number of elements equal to the number of mixtures.
- Load(string)
Loads and creates a serialized model from a file.
- LoadFromString(string)
Loads algorithm from a String.
- Predict2(InputArray, OutputArray)
Predicts the response for sample
- TrainE(InputArray, InputArray, InputArray, InputArray, OutputArray, OutputArray, OutputArray)
Estimate the Gaussian mixture parameters from a samples set.
- TrainEM(InputArray, OutputArray, OutputArray, OutputArray)
Estimate the Gaussian mixture parameters from a samples set.
- TrainM(InputArray, InputArray, OutputArray, OutputArray, OutputArray)
Estimate the Gaussian mixture parameters from a samples set.