Accord.Tests.Statistics.Models.Fields.IndependentMarkovFunctionTest.CreateModel3 C# (CSharp) Method

        public static HiddenMarkovClassifier<Independent> CreateModel3(out double[][][] sequences2, out int[] labels2)
        {
            sequences2 = new double[][][]
            {
                new double[][] 
                { 
                    // This is the first  sequence with label = 0
                    new double[] { 1, 1.12, 2.41, 1.17, 9.3 },
                    new double[] { 1, 2.54, 1.45, 0.16, 4.5 },
                    new double[] { 1, 3.46, 2.63, 1.15, 9.2 },
                    new double[] { 1, 4.73, 0.41, 1.54, 5.5 },
                    new double[] { 2, 5.81, 2.42, 1.13, 9.1 },
                }, 

                new double[][] 
                { 
                    // This is the first  sequence with label = 0
                    new double[] { 0, 1.49, 2.48, 1.18, 9.37 },
                    new double[] { 1, 2.18, 1.44, 2.19, 1.56 },
                    new double[] { 1, 3.77, 2.62, 1.10, 9.25 },
                    new double[] { 2, 4.76, 5.44, 3.58, 5.54 },
                    new double[] { 2, 5.85, 2.46, 1.16, 5.13 },
                    new double[] { 2, 4.84, 5.44, 3.54, 5.52 },
                    new double[] { 2, 5.83, 3.41, 1.22, 5.11 },
                }, 

                new double[][] 
                { 
                    // This is the first  sequence with label = 0
                    new double[] { 2, 1.11, 2.41, 1.12, 2.31 },
                    new double[] { 1, 2.52, 3.73, 0.12, 4.50 },
                    new double[] { 1, 3.43, 2.61, 1.24, 9.29 },
                    new double[] { 1, 4.74, 2.42, 2.55, 6.57 },
                    new double[] { 2, 5.85, 2.43, 1.16, 9.16 },
                }, 

                new double[][]
                {
                        // This is the second sequence with label = 1
                    new double[] { 0,  1.26, 5.44, 1.56, 9.55 },
                    new double[] { 2,  2.67, 5.45, 4.27, 1.54 },
                    new double[] { 1,  1.28, 3.46, 2.18, 4.13 },
                    new double[] { 1,  5.89, 2.57, 1.79, 5.02 },
                    new double[] { 0,  1.40, 2.48, 2.10, 6.41 },
                },

                new double[][]
                {
                        // This is the second sequence with label = 1
                    new double[] { 2,  3.21, 2.49, 1.54, 9.17 },
                    new double[] { 2,  2.62, 5.40, 4.25, 1.54 },
                    new double[] { 1,  1.53, 6.49, 2.17, 4.52 },
                    new double[] { 1,  2.84, 2.58, 1.73, 6.04 },
                    new double[] { 1,  1.45, 2.47, 2.28, 5.42 },
                    new double[] { 1,  1.46, 2.46, 2.35, 5.41 },
                },

                new double[][]
                {
                        // This is the second sequence with label = 1
                    new double[] { 1,  5.27, 5.45, 1.4, 9.5 },
                    new double[] { 2,  2.68, 2.54, 3.2, 2.2 },
                    new double[] { 1,  2.89, 3.83, 2.6, 4.1 },
                    new double[] { 1,  1.80, 1.32, 1.2, 4.2 },
                    new double[] { 0,  1.41, 2.41, 2.1, 6.4 },
                }
            };

            labels2 = new[] { 0, 0, 0, 1, 1, 1 };

            // Create a Continuous density Hidden Markov Model Sequence Classifier
            // to detect a multivariate sequence and the same sequence backwards.
            var comp1 = new GeneralDiscreteDistribution(3);
            var comp2 = new NormalDistribution(1);
            var comp3 = new NormalDistribution(2);
            var comp4 = new NormalDistribution(3);
            var comp5 = new NormalDistribution(4);
            var density = new Independent(comp1, comp2, comp3, comp4, comp5);

            // 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<Independent>(
                2, new Forward(5), density);

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

                // Train each model until the log-likelihood changes less than 0.0001
                modelIndex => new BaumWelchLearning<Independent>(
                    classifier.Models[modelIndex])
                {
                    Tolerance = 0.0001,
                    Iterations = 0,
                }
            );

            // Train the sequence classifier using the algorithm
            double logLikelihood = teacher.Run(sequences2, labels2);

            Assert.AreEqual(-3.0493028798326081d, logLikelihood, 1e-10);

            return classifier;
        }

        public void LogBackwardTest2()
        {
            double[][][] observations;
            int[]        labels;

            var hmm = IndependentMarkovFunctionTest.CreateModel3(out observations, out labels);

            MarkovMultivariateFunction function = new MarkovMultivariateFunction(hmm);


            foreach (double[][] x in observations)
            {
                foreach (int y in labels)
                {
                    double[,] actual = new double[x.Length, 5];
                    Accord.Statistics.Models.Fields.
                    ForwardBackwardAlgorithm.LogBackward(function.Factors[y], x, y, actual);

                    double[,] expected = new double[x.Length, 5];
                    Accord.Statistics.Models.Markov.
                    ForwardBackwardAlgorithm.LogBackward(hmm.Models[y], x, expected);

                    for (int i = 0; i < actual.GetLength(0); i++)
                    {
                        for (int j = 0; j < actual.GetLength(1); j++)
                        {
                            Assert.AreEqual(expected[i, j], actual[i, j], 1e-10);
                            Assert.IsFalse(Double.IsNaN(actual[i, j]));
                        }
                    }
                }
            }
        }