The GaussianMixtureModel type exposes the following members.
Gets the collection of clusters currently modeled by the clustering algorithm.
Gets or sets whether cluster labels should be computed at the end of the learning iteration. Setting to False might save a few computations in case they are not necessary.
Gets or sets whether the log-likelihood should be computed at the end of the learning iteration. Setting to False might save a few computations in case they are not necessary.
Gets the Gaussian components of the mixture model.
Gets or sets how many random initializations to try. Default is 3.
Gets how many iterations have been performed in the last call to Compute(Double).
Gets the log-likelihood of the model at the last iteration.
Gets or sets the maximum number of iterations to be performed by the method. If set to zero, no iteration limit will be imposed. Default is 0.
Gets or sets the fitting options for the component Gaussian distributions of the mixture model.
Gets or sets the parallelization options for this algorithm.(Inherited from ParallelLearningBase.)
Gets or sets a cancellation token that can be used to cancel the algorithm while it is running.(Inherited from ParallelLearningBase.)
Gets or sets the convergence criterion for the Expectation-Maximization algorithm. Default is 1e-3.
Gets or sets whether to make computations using the log -domain. This might improve accuracy on large datasets.