How to create an independent Normal hidden Markov Model in Accord.NET

gistfile1.cs

// Let's say we have 2 meteorological sensors gathering data
// from different time periods of the day. Those periods are
// represented below:

double[][][] data =
{
    new double[][] // first sequence (we just repeated the measurements 
    {              //  once, so there is only one observation sequence)

        new double[] { 1, 2 }, // Day 1, 15:00 pm
        new double[] { 6, 7 }, // Day 1, 16:00 pm
        new double[] { 2, 3 }, // Day 1, 17:00 pm
        new double[] { 2, 2 }, // Day 1, 18:00 pm
        new double[] { 9, 8 }, // Day 1, 19:00 pm
        new double[] { 1, 0 }, // Day 1, 20:00 pm
        new double[] { 1, 3 }, // Day 1, 21:00 pm
        new double[] { 8, 9 }, // Day 1, 22:00 pm
        new double[] { 3, 3 }, // Day 1, 23:00 pm
        new double[] { 1, 3 }, // Day 2, 00:00 am
        new double[] { 1, 1 }, // Day 2, 01:00 am
    }
};

// Let's assume those sensors are unrelated (for simplicity). As
// such, let's assume the data gathered from the sensors may reside
// into circular centroids denoting each state the underlying system
// might be in.
NormalDistribution[] initial_components = 
{
    new NormalDistribution(), // initial value for the first variable's distribution
    new NormalDistribution()  // initial value for the second variable's distribution
};

// Specify a initial independent normal distribution for the samples.
var density = new Independent<NormalDistribution>(initial_components);

// Creates a continuous hidden Markov Model with two states organized in an Ergodic
//  topology and an underlying independent Normal distribution as probability density.
var model = new HiddenMarkovModel<Independent<NormalDistribution>>(new Ergodic(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 BaumWelchLearning<Independent<NormalDistribution>>(model)
{
    Tolerance = 0.0001,
    Iterations = 0,
};

// Fit the model
double error = teacher.Run(data);

// Get the hidden state associated with each observation
//
double logLikelihood; // log-likelihood of the Viterbi path
int[] hidden_states = model.Decode(data[0], out logLikelihood);