Accord.Tests.Statistics.KernelPrincipalComponentAnalysisTest.TransformTest4 C# (CSharp) Method

        public void TransformTest4()
        {
            // Tested against R's kernlab

            // test <-   c(16, 117, 94, 132, 13, 73, 68, 129, 91, 50, 56, 12, 145, 105, 35, 53, 38, 51, 85, 116)
            int[] test = { 15, 116, 93, 131, 12, 72, 67, 128, 90, 49, 55, 11, 144, 104, 34, 52, 37, 50, 84, 115 };

            // data(iris)
            double[,] iris = create_iris();


            // kpc <- kpca(~.,data=iris[-test,-5],kernel="rbfdot",kpar=list(sigma=0.2),features=2)
            var data = iris.Remove(test, new[] { 4 });
            var kernel = new Gaussian() { Gamma = 1 };
            var kpc = new KernelPrincipalComponentAnalysis(data, kernel);

            kpc.Compute(2);

            var rotated = kpc.Result;
            var pcv = kpc.ComponentMatrix;
            var eig = kpc.Eigenvalues;

            double[] expected_eig = { 28.542404060412132, 15.235596653653861 };
            double[,] expected_pcv = expected_r();

            Assert.IsTrue(Matrix.IsEqual(expected_eig, eig, 1e-10));
            Assert.IsTrue(Matrix.IsEqual(expected_pcv, pcv, 1e-10));

            double[,] irisSubset = iris_sub();

            var testing = iris.Submatrix(test, new[] { 0, 1, 2, 3 });

            Assert.IsTrue(Matrix.IsEqual(irisSubset, testing));


            double[,] proj = kpc.Transform(testing);

            double[,] expectedProjection = expected_p();

            Assert.IsTrue(expectedProjection.IsEqual(proj, 1e-6));
        }