expectation_maximization.rst



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 :ref:`ML_Expectation Maximization`.

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
The default constructor represents a rough rule-of-the-thumb:
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 problem-dependent parameter) to initial values for the mixture parameters.

CvEM

CvEM::train
Estimates the Gaussian mixture parameters from a sample set.
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:
p_{i,k}
 in probs,
a_k
 in means ,
S_k
 in covs[k],
\pi_k
 in weights , and optionally computes the output "class label" for each sample:
\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N
 (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
:ocv:class:`CvNormalBayesClassifier`.
For an example of clustering random samples of the multi-Gaussian distribution using EM, see em.cpp sample in the OpenCV distribution.

CvEM::predict
Returns a mixture component index of a sample.

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

CvEM::getMeans
Returns mixture means a_k
.

CvEM::getCovs
Returns mixture covariance matrices S_k
.

CvEM::getWeights
Returns mixture weights \pi_k
.

CvEM::getProbs
Returns vectors of probabilities for each training sample.
For each training sample i
 (that have been passed to the constructor or to :ocv:func:`CvEM::train`) returns probabilities p_{i,k}
 to belong to a mixture component k
.

CvEM::getLikelihood
Returns logarithm of likelihood.

CvEM::write
Writes the trained Gaussian mixture model to the file storage.

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