trix of such type for the further computing.
.       @param means0 Initial means \f$a_k\f$ of mixture components. It is a one-channel matrix of
.           \f$nclusters \times dims\f$ size. If the matrix does not have CV_64F type it will be
.           converted to the inner matrix of such type for the further computing.
.       @param covs0 The vector of initial covariance matrices \f$S_k\f$ of mixture components. Each of
.           covariance matrices is a one-channel matrix of \f$dims \times dims\f$ size. If the matrices
.           do not have CV_64F type they will be converted to the inner matrices of such type for the
.           further computing.
.       @param weights0 Initial weights \f$\pi_k\f$ of mixture components. It should be a one-channel
.           floating-point matrix with \f$1 \times nclusters\f$ or \f$nclusters \times 1\f$ size.
.       @param logLikelihoods The optional output matrix that contains a likelihood logarithm value for
.           each sample. It has \f$nsamples \times 1\f$ size and CV_64FC1 type.
.       @param labels The optional output "class label" for each sample:
.           \f$\texttt{labels}_i=\texttt{arg max}_k(p_{i,k}), i=1..N\f$ (indices of the most probable
.           mixture component for each sample). It has \f$nsamples \times 1\f$ size and CV_32SC1 type.
.       @param probs The optional output matrix that contains posterior probabilities of each Gaussian
.           mixture component given the each sample. It has \f$nsamples \times nclusters\f$ size and
.           CV_64FC1 type.