ViterbiLearningTDistribution Properties

Gets or sets on how many batches the learning data should be divided during learning. Batches are used to estimate adequately the first models so they can better compute the Viterbi paths for subsequent passes of the algorithm. Default is 1.

Gets the current iteration.

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

Gets a value indicating whether this instance has converged.

Please use MaxIterations instead.

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

Gets the model being trained.

Gets or sets a cancellation token that can be used to stop the learning 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 whether to use Laplace's rule of succession to avoid zero probabilities.