Accord.Tests.Statistics.Models.Fields.ForwardBackwardAlgorithmTest.LogForwardTest3 C# (CSharp) Method

        public void LogForwardTest3()
        {
            MultivariateNormalDistribution density = new MultivariateNormalDistribution(3);
            var hmm = new HiddenMarkovClassifier<MultivariateNormalDistribution>(2, new Ergodic(2), density);

            double[][][] inputs =
            {
                new [] { new double[] { 0, 1, 0 }, new double[] { 0, 1, 0 }, new double[] { 0, 1, 0 } },
                new [] { new double[] { 1, 6, 2 }, new double[] { 2, 1, 6 }, new double[] { 1, 1, 0 } },
                new [] { new double[] { 9, 1, 0 }, new double[] { 0, 1, 5 }, new double[] { 0, 0, 0 } },
            };

            int[] outputs = 
            {
                0, 0, 1
            };

            var function = new MarkovMultivariateFunction(hmm);

            var observations = inputs[0];

            double[,] expected = Matrix.Log(Accord.Statistics.Models.Fields.
                ForwardBackwardAlgorithm.Forward(function.Factors[0], observations, 0));

            double logLikelihood;
            double[,] actual = Accord.Statistics.Models.Fields.
                ForwardBackwardAlgorithm.LogForward(function.Factors[0], observations, 0, out logLikelihood);

            Assert.IsTrue(expected.IsEqual(actual, 1e-10));

            double p = 0;
            for (int i = 0; i < hmm[0].States; i++)
                p += Math.Exp(actual[observations.Length - 1, i]);

            Assert.AreEqual(Math.Exp(logLikelihood), p, 1e-8);
            Assert.IsFalse(double.IsNaN(p));
        }