Specifying regularization options for Gaussian HMM fitting

    // Create a degenerate problem
    double[][] sequences = new double[][]
    {
        new double[] { 1,1,1,1,1,0,1,1,1,1 },
        new double[] { 1,1,1,1,0,1,1,1,1,1 },
        new double[] { 1,1,1,1,1,1,1,1,1,1 },
        new double[] { 1,1,1,1,1,1         },
        new double[] { 1,1,1,1,1,1,1       },
        new double[] { 1,1,1,1,1,1,1,1,1,1 },
        new double[] { 1,1,1,1,1,1,1,1,1,1 },
    };

    // Creates a continuous hidden Markov Model with two states organized in a ergodic
    //  topology and an underlying multivariate Normal distribution as density.
    var density = new Accord.Statistics.Distributions.Multivariate.NormalDistribution(1);
    ContinuousHiddenMarkovModel model = new ContinuousHiddenMarkovModel(new Ergodic(2), density);

    // Configure the learning algorithms to train the sequence classifier
    ContinuousBaumWelchLearning teacher = new ContinuousBaumWelchLearning(model)
    {
        Tolerance = 0.0001,
        Iterations = 0,

        // Configure options for fitting the normal distribution
        FittingOptions = new NormalOptions() { Regularization = 0.0001, }
    };

    // Fit the model. No exceptions will be thrown
    double likelihood = teacher.Run(sequences);