Table of Contents

Method TrainE

Namespace
OpenCvSharp
Assembly
OpenCvSharp.dll

TrainE(InputArray, InputArray, InputArray, InputArray, OutputArray, OutputArray, OutputArray)

Estimate the Gaussian mixture parameters from a samples set.

public virtual bool TrainE(InputArray samples, InputArray means0, InputArray covs0 = default, InputArray weights0 = default, OutputArray logLikelihoods = default, OutputArray labels = default, OutputArray probs = default)

Parameters

samples InputArray

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 CV_64F type it will be converted to the inner matrix of such type for the further computing.

means0 InputArray

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.

covs0 InputArray

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.

weights0 InputArray

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.

logLikelihoods OutputArray

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.

labels OutputArray

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.

probs OutputArray

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.

Returns

bool