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.
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.
The constructors
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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.
The class implements the EM algorithm as described in the beginning of the section Expectation Maximization.
Estimates the Gaussian mixture parameters from a sample set.
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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 multi-Gaussian distribution using EM, see em.cpp sample in the OpenCV distribution.
Returns a mixture component index of a sample.
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Returns the number of mixture components in the Gaussian mixture model.
Returns mixture means .
Returns mixture covariance matrices .
Returns mixture weights .
Returns vectors of probabilities for each training sample.
For each training sample (that have been passed to the constructor or to CvEM::train()) returns probabilities to belong to a mixture component .
Returns logarithm of likelihood.
Writes the trained Gaussian mixture model to the file storage.
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Reads the trained Gaussian mixture model from the file storage.
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