HiddenMarkovClassifierLearningTDistribution, TObservation Methods

Creates an instance of the model to be learned. Inheritors of this abstract class must define this method so new models can be created from the training data.

Creates the state transition topology for the threshold model. This method can be used to help in the implementation of the Threshold abstract method which has to be defined for implementers of this class.

Determines whether the specified object is equal to the current object.

Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.

Serves as the default hash function.

Gets the Type of the current instance.

Learns a model that can map the given inputs to the given outputs.

Creates a shallow copy of the current Object.

Raises the [E:GenerativeClassModelLearningFinished] event.

Raises the [E:GenerativeClassModelLearningStarted] event.

Trains each model to recognize each of the output labels.

Creates a new threshold model for the current set of Markov models in this sequence classifier.

Returns a string that represents the current object.

Checks whether an object implements a method with the given name.

Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices.

Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime.

Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime.