Accord.Tests.Statistics.GenericHiddenMarkovModelTest2.LearnTest10 C# (CSharp) Method

        public void LearnTest10()
        {
            // Create sequences of vector-valued observations. In the
            // sequence below, a single observation is composed of two
            // coordinate values, such as (x, y). There seems to be two
            // states, one for (x,y) values less than (5,5) and another
            // for higher values. The states seems to be switched on
            // every observation.
            double[][][] sequences =
            {
                new double[][] // sequence 1
                {
                    new double[] { 1, 2 }, // observation 1 of sequence 1
                    new double[] { 6, 7 }, // observation 2 of sequence 1
                    new double[] { 2, 3 }, // observation 3 of sequence 1
                },
                new double[][] // sequence 2
                {
                    new double[] { 2, 2 }, // observation 1 of sequence 2
                    new double[] { 9, 8 }, // observation 2 of sequence 2
                    new double[] { 1, 0 }, // observation 3 of sequence 2
                },
                new double[][] // sequence 3
                {
                    new double[] { 1, 3 }, // observation 1 of sequence 3
                    new double[] { 8, 9 }, // observation 2 of sequence 3
                    new double[] { 3, 3 }, // observation 3 of sequence 3
                },
            };


            // Specify a initial normal distribution for the samples.
            var density = new MultivariateNormalDistribution(dimension: 2);

            // Creates a continuous hidden Markov Model with two states organized in a forward
            //  topology and an underlying univariate Normal distribution as probability density.
            var model = new HiddenMarkovModel<MultivariateNormalDistribution, double[]>(new Forward(2), density);

            // Configure the learning algorithms to train the sequence classifier until the
            // difference in the average log-likelihood changes only by as little as 0.0001
            var teacher = new BaumWelchLearning<MultivariateNormalDistribution, double[]>(model)
            {
                Tolerance = 0.0001,
                Iterations = 0,
            };

            // Fit the model
            teacher.Learn(sequences);
            double logLikelihood = teacher.LogLikelihood;

            // See the likelihood of the sequences learned
            double a1 = Math.Exp(model.Evaluate(new[] { 
                new double[] { 1, 2 }, 
                new double[] { 6, 7 },
                new double[] { 2, 3 }})); // 0.000208

            double a2 = Math.Exp(model.Evaluate(new[] { 
                new double[] { 2, 2 }, 
                new double[] { 9, 8  },
                new double[] { 1, 0 }})); // 0.0000376

            // See the likelihood of an unrelated sequence
            double a3 = Math.Exp(model.Evaluate(new[] { 
                new double[] { 8, 7 }, 
                new double[] { 9, 8  },
                new double[] { 1, 0 }})); // 2.10 x 10^(-89)

            Assert.AreEqual(0.00020825319093038984, a1);
            Assert.AreEqual(0.000037671116792519834, a2, 1e-15);
            Assert.AreEqual(2.1031924118199194E-89, a3);
        }