Estimate the Gaussian mixture parameters from a samples set, starting from the Maximization step

[logLikelihoods, labels, probs] = model.trainM(samples, probs0)

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.
• probs0 Initial probabilities p_{i,k} of sample i to belong to mixture component k. It is a one-channel floating-point matrix of nsamples-by-ClustersNumber size.

Output

• logLikelihoods See the cv.EM.trainEM method.
• labels See the cv.EM.trainEM method.
• probs See the cv.EM.trainEM method.

This variation starts with Maximization step. You need to provide initial probabilities p_{i,k} to use this option.