HiddenMarkovClassifierLearning Class

public class HiddenMarkovClassifierLearning : BaseHiddenMarkovClassifierLearning<HiddenMarkovClassifier, HiddenMarkovModel, GeneralDiscreteDistribution, int>

Public Class HiddenMarkovClassifierLearning
	Inherits BaseHiddenMarkovClassifierLearning(Of HiddenMarkovClassifier, HiddenMarkovModel, GeneralDiscreteDistribution, Integer)

// Declare some testing data
int[][] inputs = new int[][]
{
    new int[] { 0,1,2,0 },   // Class 0
    new int[] { 0,0,2,0 },   // Class 0
    new int[] { 0,1,2,1,0 }, // Class 0
    new int[] { 0,1,2,0 },     // Class 0

    new int[] { 1,0,2,1 },   // Class 1
    new int[] { 1,1,2,1 },   // Class 1
    new int[] { 1,0,2,0,1 }, // Class 1
    new int[] { 1,0,2,1 },     // Class 1
};

int[] outputs = new int[]
{
    0,0,0,0, // First four sequences are of class 0
    1,1,1,1, // Last four sequences are of class 1
};

// Create a new learning algorithm to train the sequence classifier
var teacher = new HiddenMarkovClassifierLearning()
{
    // Train each model until the log-likelihood changes less than 0.001
    Learner = (i) => new BaumWelchLearning()
    {
        Tolerance = 0.001,
        Iterations = 0,
        NumberOfStates = 2,
    }
};

// Train the sequence classifier 
HiddenMarkovClassifier classifier = teacher.Learn(inputs, outputs);

// Obtain classification labels for the output
int[] predicted = classifier.Decide(inputs);

// Obtain prediction scores for the outputs
double[] lls = classifier.LogLikelihood(inputs);

// Declare some testing data
int[][] inputs = new int[][]
{
    new int[] { 0,0,1,2 },     // Class 0
    new int[] { 0,1,1,2 },     // Class 0
    new int[] { 0,0,0,1,2 },   // Class 0
    new int[] { 0,1,2,2,2 },   // Class 0

    new int[] { 2,2,1,0 },     // Class 1
    new int[] { 2,2,2,1,0 },   // Class 1
    new int[] { 2,2,2,1,0 },   // Class 1
    new int[] { 2,2,2,2,1 },   // Class 1
};

int[] outputs = new int[]
{
    0,0,0,0, // First four sequences are of class 0
    1,1,1,1, // Last four sequences are of class 1
};


// Create a new learning algorithm to train the sequence classifier
var teacher = new HiddenMarkovClassifierLearning()
{
    Learner = (i) => new BaumWelchLearning()
    {
        NumberOfStates = 3,
        Tolerance = 0.001,
        Iterations = 0,
    },

    Rejection = true // Enable support for sequence rejection
};

// Train the sequence classifier 
var classifier = teacher.Learn(inputs, outputs);

// Obtain prediction classes for the outputs
int[] prediction = classifier.Decide(inputs);

// Obtain prediction scores for the outputs
double[] lls = classifier.LogLikelihood(inputs);