Accord.Tests.Statistics.GaussianLinearTest.FunctionTest2 C# (CSharp) Method

        public void FunctionTest2()
        {
            // Tested against R's kernlab

            double[][] data = 
            {
                new double[] { 5.1, 3.5, 1.4, 0.2 },
                new double[] { 5.0, 3.6, 1.4, 0.2 },
                new double[] { 4.9, 3.0, 1.4, 0.2 },
                new double[] { 5.8, 4.0, 1.2, 0.2 },
                new double[] { 4.7, 3.2, 1.3, 0.2 },
            };

            // rbf <- rbfdot(sigma = 1)

            // R's sigma is framework's Gaussian's gamma:
            var kernel = new Gaussian<Linear>(new Linear(0)) { Gamma = 1 };

            // Compute the kernel matrix
            double[,] actual = new double[5, 5];
            for (int i = 0; i < 5; i++)
                for (int j = 0; j < 5; j++)
                    actual[i, j] = kernel.Function(data[i], data[j]);

            double[,] expected =
            {
                { 1.0000000, 0.9801987, 0.7482636, 0.4584060, 0.7710516 },
                { 0.9801987, 1.0000000, 0.6907343, 0.4317105, 0.7710516 },
                { 0.7482636, 0.6907343, 1.0000000, 0.1572372, 0.9139312 },
                { 0.4584060, 0.4317105, 0.1572372, 1.0000000, 0.1556726 },
                { 0.7710516, 0.7710516, 0.9139312, 0.1556726, 1.0000000 },
            };

            // Assert both are equal
            for (int i = 0; i < 5; i++)
                for (int j = 0; j < 5; j++)
                    Assert.AreEqual(expected[i, j], actual[i, j], 1e-6);
        }