Accord.Tests.Statistics.GenericHiddenMarkovModelTest.LearnIntegersTest5 C# (CSharp) Method

        public void LearnIntegersTest5()
        {

            int[][][] sequences = new int[][][] 
            {
                new int[][] { new int[] { 0 }, new int[] { 3 }, new int[] { 1} },
                new int[][] { new int[] { 0 }, new int[] { 2 } },
                new int[][] { new int[] { 1 }, new int[] { 0 }, new int[] { 3 } },
                new int[][] { new int[] { 3 }, new int[] { 4 } },
                new int[][] { new int[] { 0 }, new int[] { 1 }, new int[] { 3 }, new int[] { 5 } },
                new int[][] { new int[] { 0 }, new int[] { 3 }, new int[] { 4 } },
                new int[][] { new int[] { 0 }, new int[] { 1 }, new int[] { 3 }, new int[] { 5 } },
                new int[][] { new int[] { 0 }, new int[] { 1 }, new int[] { 3 }, new int[] { 5 } },
                new int[][] { new int[] { 0 }, new int[] { 1 }, new int[] { 3 }, new int[] { 4 }, new int[] { 5 } },
            };

            var hmm = HiddenMarkovModel.CreateGeneric(3, 6);

            Assert.AreEqual(3, hmm.States);
            Assert.AreEqual(3, hmm.Emissions.Length);
            for (int i = 0; i < 3; i++)
                Assert.AreEqual(6, hmm.Emissions[i].Length);

            var teacher = new BaumWelchLearning<GeneralDiscreteDistribution>(hmm) { Iterations = 100, Tolerance = 0 };
            double ll = teacher.Run(sequences);

            double l0; hmm.Decode(sequences[0], out l0);
            double l1; hmm.Decode(sequences[1], out l1);
            double l2; hmm.Decode(sequences[2], out l2);

            double pl = System.Math.Exp(ll);
            double p0 = System.Math.Exp(l0);
            double p1 = System.Math.Exp(l1);
            double p2 = System.Math.Exp(l2);

            Assert.AreEqual(0.82996841576789704, pl, 1e-6);
            Assert.AreEqual(0.014012065043262257, p0, 1e-6);
            Assert.AreEqual(0.016930905415294066, p1, 1e-6);
            Assert.AreEqual(0.0019365959189660638, p2, 1e-6);

            Assert.AreEqual(1, hmm.Dimension);




            int[][] sequences2 = new int[][] 
            {
                new int[] { 0, 3, 1 },
                new int[] { 0, 2 },
                new int[] { 1, 0, 3 },
                new int[] { 3, 4 },
                new int[] { 0, 1, 3, 5 },
                new int[] { 0, 3, 4 },
                new int[] { 0, 1, 3, 5 },
                new int[] { 0, 1, 3, 5 },
                new int[] { 0, 1, 3, 4, 5 },
            };

            hmm = HiddenMarkovModel.CreateGeneric(3, 6);

            teacher = new BaumWelchLearning<GeneralDiscreteDistribution>(hmm) { Iterations = 100 };
            double ll2 = teacher.Run(sequences2);

            double l02; hmm.Decode(sequences2[0], out l02);
            double l12; hmm.Decode(sequences2[1], out l12);
            double l22; hmm.Decode(sequences2[2], out l22);

            Assert.AreEqual(ll, ll2);
            Assert.AreEqual(l0, l02);
            Assert.AreEqual(l1, l12);
            Assert.AreEqual(l2, l22);

            Assert.AreEqual(1, hmm.Dimension);
        }