Mixture HMM example

using Accord.Statistics.Distributions.Univariate;
using Accord.Statistics.Models.Markov;
using Accord.Statistics.Models.Markov.Learning;
using Accord.Statistics.Models.Markov.Topology;

using Multivariate = Accord.Statistics.Distributions.Multivariate;

namespace Test
{

//...

    // Create a Continuous density Hidden Markov Model Sequence Classifier
    // to detect a multivariate sequence and the same sequence backwards.
    double[][][] sequences = new double[][][]
    {
        new double[][]
        {
            // This is the first  sequence with label = 0
            new double[] { 0 },
            new double[] { 1 },
            new double[] { 2 },
            new double[] { 3 },
            new double[] { 4 },
        },

        new double[][]
        {
                // This is the second sequence with label = 1
            new double[] { 4 },
            new double[] { 3 },
            new double[] { 2 },
            new double[] { 1 },
            new double[] { 0 },
        }
    };

    // Labels for the sequences
    int[] labels = { 0, 1 };


    // Create a mixture of two 1-dimensional normal distributions (by default,
    // initialized with zero mean and unit covariance matrices).
    var density = new Multivariate.Mixture<Multivariate.NormalDistribution>(
        new Multivariate.NormalDistribution(1),
        new Multivariate.NormalDistribution(1));

    // Creates a sequence classifier containing 2 hidden Markov Models with 2 states
    // and an underlying multivariate mixture of Normal distributions as density.
    var classifier = new ContinuousSequenceClassifier(2, new Ergodic(2), density);

    // Configure the learning algorithms to train the sequence classifier
    var teacher = new ContinuousSequenceClassifierLearning(classifier,

        // Train each model until the log-likelihood changes less than 0.0001
        modelIndex => new ContinuousBaumWelchLearning(classifier.Models[modelIndex])
        {
            Tolerance = 0.0001,
            Iterations = 0,
        }
    );

    // Train the sequence classifier using the algorithm
    double logLikelihood = teacher.Run(sequences, labels);


    // Calculate the probability that the given
    //  sequences originated from the model
    double likelihood1, likelihood2;

    // Try to classify the 1st sequence (output should be 0)
    int c1 = classifier.Compute(sequences[0], out likelihood1);

    // Try to classify the 2nd sequence (output should be 1)
    int c2 = classifier.Compute(sequences[1], out likelihood2);

    // ...

}