Accord.Tests.Statistics.GenericSequenceClassifierTest.LearnTest1 C# (CSharp) Method

        public void LearnTest1()
        {
            // Create a Continuous density Hidden Markov Model Sequence Classifier
            // to detect a univariate sequence and the same sequence backwards.
            double[][] sequences = new double[][] 
            {
                new double[] { 0,1,2,3,4 }, // This is the first  sequence with label = 0
                new double[] { 4,3,2,1,0 }, // This is the second sequence with label = 1
            };

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

            // Creates a sequence classifier containing 2 hidden Markov Models
            //  with 2 states and an underlying Normal distribution as density.
            NormalDistribution density = new NormalDistribution();
            var classifier = new HiddenMarkovClassifier<NormalDistribution>(2, new Ergodic(2), density);

            // Configure the learning algorithms to train the sequence classifier
            var teacher = new HiddenMarkovClassifierLearning<NormalDistribution>(classifier,

                // Train each model until the log-likelihood changes less than 0.001
                modelIndex => new BaumWelchLearning<NormalDistribution>(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 first sequence (output should be 0)
            int c1 = classifier.Compute(sequences[0], out likelihood1);

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

            Assert.AreEqual(0, c1);
            Assert.AreEqual(1, c2);


            Assert.AreEqual(-13.271981026832929, logLikelihood, 1e-10);
            Assert.AreEqual(0.99999791320102149, likelihood1, 1e-10);
            Assert.AreEqual(0.99999791320102149, likelihood2, 1e-10);
            Assert.IsFalse(double.IsNaN(logLikelihood));
            Assert.IsFalse(double.IsNaN(likelihood1));
            Assert.IsFalse(double.IsNaN(likelihood2));
        }