BaumWelchLearningTDistribution, TObservation Methods

Computes the forward and backward probabilities matrices for a given observation referenced by its index in the input training data.

Computes the ksi matrix of probabilities for a given observation referenced by its index in the input training data.

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.

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.

Fits one emission distribution. This method can be override in a base class in order to implement special fitting options.

Serves as the default hash function.

Gets the Type of the current instance.

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

Creates a shallow copy of the current Object.

Returns a string that represents the current object.

Updates the emission probability matrix.

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.