EM Class

Creates instance by pointer cv::Ptr<EM>

Gets or sets a memory address allocated by AllocMemory.

Gets or sets the byte length of the allocated memory

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.

Constraint on covariance matrices which defines type of matrices.

Native pointer of OpenCV structure

Gets or sets a handle which allocates using cvSetData.

Returns true if the Algorithm is empty (e.g. in the very beginning or after unsuccessful read

Gets a value indicating whether this instance has been disposed.

Gets or sets a value indicating whether you permit disposing this instance.

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.

Pins the object to be allocated by cvSetData.

Allocates the specified size of memory.

Creates empty EM model.

Releases the resources

Releases the resources

Releases managed resources

releases unmanaged resources

Determines whether the specified object is equal to the current object.

Destructor

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.

Returns the algorithm string identifier. This string is used as top level xml/yml node tag when the object is saved to a file or string.

Serves as the default hash function.

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.

Gets the Type of the current instance.

Returns weights of the mixtures. Returns vector with the number of elements equal to the number of mixtures.

Loads and creates a serialized model from a file.

Loads algorithm from a String.

Creates a shallow copy of the current Object.

Notifies the allocated size of memory.

Predicts the response for sample

Reads algorithm parameters from a file storage

Saves the algorithm to a file. In order to make this method work, the derived class must implement Algorithm::write(FileStorage fs).

If this object is disposed, then ObjectDisposedException is thrown.

Returns a string that represents the current object.

Estimate the Gaussian mixture parameters from a samples set.

Estimate the Gaussian mixture parameters from a samples set.

Estimate the Gaussian mixture parameters from a samples set.

Stores algorithm parameters in a file storage

Data pointer