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

[logLikelihoods, labels, probs] = model.trainE(samples, means0)
[...] = model.trainE(..., 'OptionName', optionValue, ...)

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.
• means0 Initial means a_k of mixture components. It is a one-channel matrix of ClustersNumber-by-dims size. 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 See the cv.EM.trainEM method.
• labels See the cv.EM.trainEM method.
• probs See the cv.EM.trainEM method.

Options

• Covs0 The vector of initial covariance matrices S_k of mixture components. Each of covariance matrices is a one-channel matrix of dims-by-dims size. If the matrices do not have double type they will be converted to the inner matrices of such type for the further computing.
• Weights0 Initial weights PI_k of mixture components. It should be a one-channel floating-point vector of length ClustersNumber.

This variation starts with Expectation step. You need to provide initial means a_k of mixture components. Optionally you can pass initial weights PI_k and covariance matrices S_k of mixture components.