Expectation Maximization
This section describes obsolete C interface of EM algorithm. Details of the algorithm and its C++ interface can be found in the other section Expectation Maximization.
Note
 An example on using the Expectation Maximalization algorithm can be found at opencv_source_code/samples/cpp/em.cpp
 (Python) An example using Expectation Maximalization for Gaussian Mixing can be found at opencv_source_code/samples/python2/gaussian_mix.py
CvEMParams

struct CvEMParams
Parameters of the EM algorithm. All parameters are public. You can initialize them by a constructor and then override some of them directly if you want.
CvEMParams::CvEMParams
The constructors

C++: CvEMParams::CvEMParams()

C++: CvEMParams::CvEMParams(int nclusters, int cov_mat_type=EM::COV_MAT_DIAGONAL, int start_step=EM::START_AUTO_STEP, CvTermCriteria term_crit=cvTermCriteria(CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 100, FLT_EPSILON), const CvMat* probs=0, const CvMat* weights=0, const CvMat* means=0, const CvMat** covs=0 )

The default constructor represents a rough ruleofthethumb:
CvEMParams() : nclusters(10), cov_mat_type(1/*CvEM::COV_MAT_DIAGONAL*/),
start_step(0/*CvEM::START_AUTO_STEP*/), probs(0), weights(0), means(0), covs(0)
{
term_crit=cvTermCriteria( CV_TERMCRIT_ITER+CV_TERMCRIT_EPS, 100, FLT_EPSILON );
}
With another constructor it is possible to override a variety of parameters from a single number of mixtures (the only essential problemdependent parameter) to initial values for the mixture parameters.
CvEM

class CvEM : public CvStatModel
The class implements the EM algorithm as described in the beginning of the section Expectation Maximization.
CvEM::train
Estimates the Gaussian mixture parameters from a sample set.

C++: bool CvEM::train(const Mat& samples, const Mat& sampleIdx=Mat(), CvEMParams params=CvEMParams(), Mat* labels=0 )

C++: bool CvEM::train(const CvMat* samples, const CvMat* sampleIdx=0, CvEMParams params=CvEMParams(), CvMat* labels=0 )
Parameters: 
 samples – Samples from which the Gaussian mixture model will be estimated.
 sampleIdx – Mask of samples to use. All samples are used by default.
 params – Parameters of the EM algorithm.
 labels – The optional output “class label” for each sample: (indices of the most probable mixture component for each sample).

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:
in probs,
in means ,
in covs[k],
in weights , and optionally computes the output “class label” for each sample:
(indices of the most probable mixture component for each sample).
The trained model can be used further for prediction, just like any other classifier. The trained model is similar to the
CvNormalBayesClassifier.
For an example of clustering random samples of the multiGaussian distribution using EM, see em.cpp sample in the OpenCV distribution.
CvEM::predict
Returns a mixture component index of a sample.

C++: float CvEM::predict(const Mat& sample, Mat* probs=0 ) const

C++: float CvEM::predict(const CvMat* sample, CvMat* probs) const
Parameters: 
 sample – A sample for classification.
 probs – If it is not null then the method will write posterior probabilities of each component given the sample data to this parameter.

CvEM::getNClusters
Returns the number of mixture components in the Gaussian mixture model.

C++: int CvEM::getNClusters() const

C++: int CvEM::get_nclusters() const
CvEM::getMeans
Returns mixture means .

C++: Mat CvEM::getMeans() const

C++: const CvMat* CvEM::get_means() const
CvEM::getCovs
Returns mixture covariance matrices .

C++: void CvEM::getCovs(std::vector<cv::Mat>& covs) const

C++: const CvMat** CvEM::get_covs() const
CvEM::getWeights
Returns mixture weights .

C++: Mat CvEM::getWeights() const

C++: const CvMat* CvEM::get_weights() const
CvEM::getProbs
Returns vectors of probabilities for each training sample.

C++: Mat CvEM::getProbs() const

C++: const CvMat* CvEM::get_probs() const
For each training sample (that have been passed to the constructor or to CvEM::train()) returns probabilities to belong to a mixture component .
CvEM::getLikelihood
Returns logarithm of likelihood.

C++: double CvEM::getLikelihood() const

C++: double CvEM::get_log_likelihood() const
CvEM::write
Writes the trained Gaussian mixture model to the file storage.

C++: void CvEM::write(CvFileStorage* fs, const char* name) const
Parameters: 
 fs – A file storage where the model will be written.
 name – A name of the file node where the model data will be written.

CvEM::read
Reads the trained Gaussian mixture model from the file storage.

C++: void CvEM::read(CvFileStorage* fs, CvFileNode* node)
Parameters: 
 fs – A file storage with the trained model.
 node – The parent map. If it is NULL, the function searches a node with parameters in all the toplevel nodes (streams), starting with the first one.
