Accord.Tests.Statistics.GenericHiddenMarkovModelTest.FittingOptionsTest C# (CSharp) Méthode

        public void FittingOptionsTest()
        {
            // Create a degenerate problem
            double[][] sequences = new double[][] 
            {
                new double[] { 1,1,1,1,1,0,1,1,1,1 },
                new double[] { 1,1,1,1,0,1,1,1,1,1 },
                new double[] { 1,1,1,1,1,1,1,1,1,1 },
                new double[] { 1,1,1,1,1,1         },
                new double[] { 1,1,1,1,1,1,1       },
                new double[] { 1,1,1,1,1,1,1,1,1,1 },
                new double[] { 1,1,1,1,1,1,1,1,1,1 },
            };

            // Creates a continuous hidden Markov Model with two states organized in a ergodic
            //  topology and an underlying multivariate Normal distribution as density.
            var density = new MultivariateNormalDistribution(1);

            var model = new HiddenMarkovModel<MultivariateNormalDistribution>(new Ergodic(2), density);

            // Configure the learning algorithms to train the sequence classifier
            var teacher = new BaumWelchLearning<MultivariateNormalDistribution>(model)
            {
                Tolerance = 0.0001,
                Iterations = 0,

                // Configure options for fitting the normal distribution
                FittingOptions = new NormalOptions() { Regularization = 0.0001, }
            };

            // Fit the model. No exceptions will be thrown
            double logLikelihood = teacher.Run(sequences);
            double likelihood = Math.Exp(logLikelihood);

            Assert.AreEqual(5.3782215178437722, logLikelihood, 1e-15);
            Assert.IsFalse(double.IsNaN(logLikelihood));

            Assert.AreEqual(0.0001, (teacher.FittingOptions as NormalOptions).Regularization);



            // Try without a regularization constant to get an exception
            bool thrown;

            thrown = false;
            density = new MultivariateNormalDistribution(1);
            model = new HiddenMarkovModel<MultivariateNormalDistribution>(new Ergodic(2), density);
            teacher = new BaumWelchLearning<MultivariateNormalDistribution>(model) { Tolerance = 0.0001, Iterations = 0, };
            Assert.IsNull(teacher.FittingOptions);
            try { teacher.Run(sequences); }
            catch { thrown = true; }
            Assert.IsTrue(thrown);

            thrown = false;
            density = new Accord.Statistics.Distributions.Multivariate.MultivariateNormalDistribution(1);
            model = new HiddenMarkovModel<MultivariateNormalDistribution>(new Ergodic(2), density);
            teacher = new BaumWelchLearning<MultivariateNormalDistribution>(model)
            {
                Tolerance = 0.0001,
                Iterations = 0,
                FittingOptions = new NormalOptions() { Regularization = 0 }
            };
            Assert.IsNotNull(teacher.FittingOptions);
            try { teacher.Run(sequences); }
            catch { thrown = true; }
            Assert.IsTrue(thrown);
        }