Accord.Tests.Statistics.Models.Fields.HiddenConditionalRandomFieldTest.ComputeTest C# (CSharp) Method

        public void ComputeTest()
        {
            HiddenMarkovClassifier hmm = DiscreteHiddenMarkovClassifierPotentialFunctionTest.CreateModel1();

            // Declare some testing data
            int[][] inputs = new int[][]
            {
                new int[] { 0,1,1,0 },   // Class 0
                new int[] { 0,0,1,0 },   // Class 0
                new int[] { 0,1,1,1,0 }, // Class 0
                new int[] { 0,1,0 },     // Class 0

                new int[] { 1,0,0,1 },   // Class 1
                new int[] { 1,1,0,1 },   // Class 1
                new int[] { 1,0,0,0,1 }, // Class 1
                new int[] { 1,0,1 },     // Class 1
            };

            int[] outputs = new int[]
            {
                0,0,0,0, // First four sequences are of class 0
                1,1,1,1, // Last four sequences are of class 1
            };


            var function = new MarkovDiscreteFunction(hmm);
            var target = new HiddenConditionalRandomField<int>(function);


            for (int i = 0; i < inputs.Length; i++)
            {
                int expected = hmm.Compute(inputs[i]);

                int actual = target.Compute(inputs[i]);

                double h0 = hmm.LogLikelihood(inputs[i], 0);
                double h1 = hmm.LogLikelihood(inputs[i], 1);

                double c0 = target.LogLikelihood(inputs[i], 0);
                double c1 = target.LogLikelihood(inputs[i], 1);

                Assert.AreEqual(expected, actual);
                Assert.AreEqual(h0, c0, 1e-10);
                Assert.AreEqual(h1, c1, 1e-10);

                Assert.IsFalse(double.IsNaN(c0));
                Assert.IsFalse(double.IsNaN(c1));
            }
        }