BaseBaumWelchLearningTModel, TDistribution, TObservation, TOptions Properties

Gets or sets convergence parameters.

Gets or sets the number of performed iterations.

Gets or sets the function that initializes the emission distributions in the hidden Markov Models.

Gets or sets the distribution fitting options to use when estimating distribution densities during learning.

Gets or sets whether the algorithm has converged.

Please use MaxIterations instead.

Gets the Gamma matrix of log probabilities created during the last iteration of the Baum-Welch learning algorithm.

Gets the Ksi matrix of log probabilities created during the last iteration of the Baum-Welch learning algorithm.

Gets the log-likelihood of the model at the last iteration.

Gets the sample weights in the last iteration of the Baum-Welch learning algorithm.

Gets or sets the maximum number of iterations performed by the learning algorithm.

Gets or sets the model being trained.

Gets or sets the number of states to be used when this learning algorithm needs to create new models.

Gets all observations as a single vector.

Gets or sets the parallelization options for this algorithm.

Gets or sets a cancellation token that can be used to cancel the algorithm while it is running.

Gets or sets the maximum change in the average log-likelihood after an iteration of the algorithm used to detect convergence.

Gets or sets the state transition topology to be used when this learning algorithm needs to create new models. Default is Forward.