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

        public void TransformTest_Jagged()
        {
            double[][] sourceMatrix = new double[][]
            {
                new double[] { 2.5,  2.4 },
                new double[] { 0.5,  0.7 },
                new double[] { 2.2,  2.9 },
                new double[] { 1.9,  2.2 },
                new double[] { 3.1,  3.0 },
                new double[] { 2.3,  2.7 },
                new double[] { 2.0,  1.6 },
                new double[] { 1.0,  1.1 },
                new double[] { 1.5,  1.6 },
                new double[] { 1.1,  0.9 }
            };

            // Create a new linear kernel
            IKernel kernel = new Linear();

            // Creates the Kernel Principal Component Analysis of the given data
            var kpca = new KernelPrincipalComponentAnalysis(sourceMatrix, kernel);

            // Compute the Kernel Principal Component Analysis
            kpca.Compute();

            double[] actual1 = kpca.Transform(sourceMatrix[0]);

            double[][] actual = kpca.Transform(sourceMatrix);

            double[][] expected = 
            {
                new double[] { -0.827970186,  0.175115307 },
                new double[] {  1.77758033,  -0.142857227 },
                new double[] { -0.992197494, -0.384374989 },
                new double[] { -0.274210416, -0.130417207 },
                new double[] { -1.67580142,   0.209498461 },
                new double[] { -0.912949103, -0.175282444 },
                new double[] {  0.099109437,  0.349824698 },
                new double[] {  1.14457216,  -0.046417258 },
                new double[] {  0.438046137, -0.017764629 },
                new double[] {  1.22382056,   0.162675287 },
            };

            Assert.IsTrue(Matrix.IsEqual(expected[0], actual1, 0.0001));

            // Verify both are equal with 0.001 tolerance value
            Assert.IsTrue(Matrix.IsEqual(actual, expected, 0.0001));

            // Assert the result equals the transformation of the input
            double[,] result = kpca.Result;
            double[,] projection = kpca.Transform(data);
            Assert.IsTrue(Matrix.IsEqual(result, projection, 0.000001));
        }