Accord.Tests.Statistics.QuasiNewtonLearningTest.RunTest C# (CSharp) Méthode

        public void RunTest()
        {
            int nstates = 3;
            int symbols = 3;

            int[][] sequences = new int[][] 
            {
                new int[] { 0, 1, 1, 1, 2 },
                new int[] { 0, 1, 1, 1, 2, 2, 2 },
                new int[] { 0, 0, 1, 1, 2, 2 },
                new int[] { 0, 1, 1, 1, 2, 2, 2 },
                new int[] { 0, 1, 1, 1, 2, 2 },
                new int[] { 0, 1, 1, 2, 2 },
                new int[] { 0, 0, 1, 1, 1, 2, 2 },
                new int[] { 0, 0, 0, 1, 1, 1, 2, 2 },
                new int[] { 0, 1, 1, 2, 2, 2 },
            };


            var function = new MarkovDiscreteFunction(nstates, symbols);
            var model = new ConditionalRandomField<int>(nstates, function);


            for (int i = 0; i < sequences.Length; i++)
            {
                double p;
                int[] s = sequences[i];
                int[] r = model.Compute(s, out p);
                Assert.IsFalse(s.IsEqual(r));
            }

            var target = new QuasiNewtonLearning<int>(model); 

            int[][] labels = sequences;
            int[][] observations = sequences;

            double ll0 = model.LogLikelihood(observations, labels);

            double actual = target.Run(observations, labels);

            double ll1 = model.LogLikelihood(observations, labels);

            Assert.IsTrue(ll1 > ll0);


            Assert.AreEqual(0, actual, 1e-8);

            for (int i = 0; i < sequences.Length; i++)
            {
                double p;
                int[] s = sequences[i];
                int[] r = model.Compute(s, out p);
                Assert.IsTrue(s.IsEqual(r));
            }
            
        }