Accord.Tests.Statistics.GenericSequenceClassifierTest2.LearnTest6 C# (CSharp) Method

        public void LearnTest6()
        {
            // 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, 1 },
                    new double[] { 1, 2 },
                    new double[] { 2, 3 },
                    new double[] { 3, 4 },
                    new double[] { 4, 5 },
                }, 

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

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


            var density = new MultivariateNormalDistribution(2);

            try
            {
                new HiddenMarkovClassifier<MultivariateNormalDistribution>(
                    2, new Custom(new double[2, 2], new double[2]), density);

                Assert.Fail();
            }
            catch (ArgumentException)
            {
            }

            var topology = new Custom(
                new[,] { { 1 / 2.0, 1 / 2.0 }, { 1 / 2.0, 1 / 2.0 } },
                new[] { 1.0, 0.0 });

            Array.Clear(topology.Initial, 0, topology.Initial.Length);
            Array.Clear(topology.Transitions, 0, topology.Transitions.Length);

            // 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 HiddenMarkovClassifier<MultivariateNormalDistribution, double[]>(
                2, topology, density);


            // Configure the learning algorithms to train the sequence classifier
            var teacher = new HiddenMarkovClassifierLearning<MultivariateNormalDistribution, double[]>(classifier)
            {
                // Train each model until the log-likelihood changes less than 0.0001
                Learner = modelIndex => new BaumWelchLearning<MultivariateNormalDistribution, double[]>(classifier.Models[modelIndex])
                {
                    Tolerance = 0.0001,
                    Iterations = 0,

                    FittingOptions = new NormalOptions() { Diagonal = true }
                }
            };

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


            // Calculate the probability that the given
            //  sequences originated from the model
            double response1, response2;

            // Try to classify the first sequence (output should be 0)
            int c1 = classifier.Decide(sequences[0]);
            response1 = classifier.Probability(sequences[0]);

            // Try to classify the second sequence (output should be 1)
            int c2 = classifier.Decide(sequences[1]);
            response2 = classifier.Probability(sequences[1]);

            Assert.AreEqual(double.NegativeInfinity, logLikelihood);
            Assert.AreEqual(0, response1);
            Assert.AreEqual(0, response2);
        }