Accord.Tests.Statistics.GenericSequenceClassifierTest2.LearnTest9 C# (CSharp) Method

        public void LearnTest9()
        {
            double[][][] inputs = large_gestures;
            int[] outputs = large_outputs;

            int states = 5;
            int iterations = 100;
            double tolerance = 0.01;
            bool rejection = true;
            double sensitivity = 1E-85;

            int dimension = inputs[0][0].Length;

            var hmm = new HiddenMarkovClassifier<MultivariateNormalDistribution, double[]>(2,
                new Forward(states), new MultivariateNormalDistribution(dimension));

            // Create the learning algorithm for the ensemble classifier
            var teacher = new HiddenMarkovClassifierLearning<MultivariateNormalDistribution, double[]>(hmm)
            {
                // Train each model using the selected convergence criteria
                Learner = i => new BaumWelchLearning<MultivariateNormalDistribution, double[]>(hmm.Models[i])
                {
                    Tolerance = tolerance,
                    Iterations = iterations,

                    FittingOptions = new NormalOptions()
                    {
                        Regularization = 1e-5
                    }
                }
            };

            teacher.Empirical = true;
            teacher.Rejection = rejection;

            // Run the learning algorithm
            teacher.Learn(inputs, outputs);
            double logLikelihood = teacher.LogLikelihood;

            hmm.Sensitivity = sensitivity;

            for (int i = 0; i < large_gestures.Length; i++)
            {
                int actual = hmm.Decide(large_gestures[i]);
                int expected = large_outputs[i];
                Assert.AreEqual(expected, actual);
            }
        }