var h = new MultivariateNormalDistribution[2];
            for (int i = 0; i < h.Length; i++)
            {
                h[i] = new MultivariateNormalDistribution(6);
            }
            var Hmm = new HiddenMarkovClassifier<MultivariateMixture<MultivariateNormalDistribution>>(classes.Count,
                new Forward(states), new MultivariateMixture<MultivariateNormalDistribution>(h), classes.ToArray());
            // I tried to change the topology from Forward to Ergodic and the performence is nearly the same
            // Ergodic models are commonly used to represent models in which a single (large) sequence of observations is used for training (such as when a training sequence does not have well defined starting and ending points and can potentially be infinitely long).
            // Create the learning algorithm for the ensemble classifier
            var teacher = new HiddenMarkovClassifierLearning<MultivariateMixture<MultivariateNormalDistribution>>(Hmm,

                // Train each model using the selected convergence criteria
                i => new BaumWelchLearning<MultivariateMixture<MultivariateNormalDistribution>>(Hmm.Models[i])
                {
                    Tolerance = tolerance,
                    Iterations = iterations,

                    FittingOptions = new MixtureOptions()
                    {
                        InnerOptions = new NormalOptions()
                        {
                            Diagonal = false
                        },
                        Logarithm = true,
                        Threshold = 1e-5
                        // , Diagonal = true

                    }
                }
            );