ViterbiLearningTDistribution, TObservation Class

public class ViterbiLearning<TDistribution, TObservation> : BaseViterbiLearning<TObservation[]>, 
	IUnsupervisedLearning<HiddenMarkovModel<TDistribution, TObservation>, TObservation[], int[]>
where TDistribution : Object, IFittableDistribution<TObservation>

Public Class ViterbiLearning(Of TDistribution As {Object, IFittableDistribution(Of TObservation)}, TObservation)
	Inherits BaseViterbiLearning(Of TObservation())
	Implements IUnsupervisedLearning(Of HiddenMarkovModel(Of TDistribution, TObservation), TObservation(), Integer())

Accord.Math.Random.Generator.Seed = 0;

// Create continuous sequences. In the sequences below, there
//  seems to be two states, one for values between 0 and 1 and
//  another for values between 5 and 7. The states seems to be
//  switched on every observation.
double[][] sequences = new double[][] 
{
    new double[] { 0.1, 5.2, 0.3, 6.7, 0.1, 6.0 },
    new double[] { 0.2, 6.2, 0.3, 6.3, 0.1, 5.0 },
    new double[] { 0.1, 7.0, 0.1, 7.0, 0.2, 5.6 },
};


// Specify a initial normal distribution
var density = new NormalDistribution();

// Create a continuous hidden Markov Model with two states organized in a forward
//  topology and an underlying univariate Normal distribution as probability density.
var model = new HiddenMarkovModel<NormalDistribution, double>(new Forward(2), density);

// Configure the learning algorithms to train the sequence classifier until the
// difference in the average log-likelihood changes only by as little as 0.0001
var teacher = new ViterbiLearning<NormalDistribution, double>(model)
{
    Tolerance = 0.0001,
    Iterations = 0,
};

// Fit the model
teacher.Learn(sequences);

// See the probability of the sequences learned
double a1 = model.LogLikelihood(new[] { 0.1, 5.2, 0.3, 6.7, 0.1, 6.0 }); // log(0.40)
double a2 = model.LogLikelihood(new[] { 0.2, 6.2, 0.3, 6.3, 0.1, 5.0 }); // log(0.46)

// See the probability of an unrelated sequence
double a3 = model.LogLikelihood(new[] { 1.1, 2.2, 1.3, 3.2, 4.2, 1.0 }); // log(1.42)