Estimate the Gaussian mixture parameters from a samples set

[logLikelihoods, labels, probs] = model.trainEM(samples)

Input

• samples Samples from which the Gaussian mixture model will be estimated. It should be a one-channel matrix, each row of which is a sample. If the matrix does not have double type it will be converted to the inner matrix of such type for the further computing.

Output

• logLikelihoods The optional output matrix that contains a likelihood logarithm value for each sample. It has nsamples-by-1 size and double type.
• labels The optional output "class label" for each sample: labels_i = argmax_{k}(p_{i,k}), i=1..N (indices of the most probable mixture component for each sample). It has nsamples-by-1 size and single type.
• probs The optional output matrix that contains posterior probabilities of each Gaussian mixture component given each sample. It has nsamples-by-ClustersNumber size and double type.

This variation starts with Expectation step. Initial values of the model parameters will be estimated by the k-means algorithm.

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: labels_i = argmax_{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 cv.NormalBayesClassifier.