One Class SVM - Many parameter combinations

        private static void Main(string[] args)
        {
            // Test single class SVMs for many parameter combinations.
            double kernelSigma;
            double teacherNu;
            double teacherTolerance;

            teacherTolerance = 0.00001;
            while (teacherTolerance < 10) {

                kernelSigma = 0.1;
                while (kernelSigma < 1) {

                    teacherNu = 0.1;
                    while (teacherNu < 1) {
                        TrainAndTest(kernelSigma, teacherNu, teacherTolerance);

                        teacherNu += 0.1;
                    }

                    kernelSigma += 0.1;
                }

                teacherTolerance *= 10;
            }

            Console.ReadLine();
        }

        private static void TrainAndTest(double kernelSigma, double teacherNu, double teacherTolerance)
        {
            // Generate input point cloud, a 0.6 x 0.6 square centered at 0,0.
            double[][] inputs = new double[49][];
            int inputIdx = 0;
            for (double x = -0.3; x <= 0.31; x += 0.1) {
                for (double y = -0.3; y <= 0.31; y += 0.1) {
                    inputs[inputIdx] = new double[] { x, y };
                    inputIdx++;
                }
            }


            // Generate inlier and outlier test points.
            double[][] outliers =
            {
                new double[] { 1E6, 1E6 },  // Very far
                new double[] { 0, 1E6 },    // Very far
                new double[] { 100, -100 }, // Far
                new double[] { 0, -100 },   // Far
                new double[] { -10, -10 },  // Not far
                new double[] { 0, -10 },    // Not far
            };
            double[][] inliers =
            {
                new double[] { 0, 0 },      // Origin
                new double[] { .15, .15 },  // Halfway to corner
                new double[] { -0.1, 0 },   // Comfortably inside
                new double[] { 0.25, 0 }    // Near edge
            };


            // Construct the kernel, model, and trainer, then train.
            Console.WriteLine($"Training model with parameters kernelSigma={kernelSigma.ToString("#.##")}, teacherNu={teacherNu.ToString("#.##")}, teacherTolerance={teacherTolerance}");
            var kernel = new Gaussian(kernelSigma);
            var svm = new KernelSupportVectorMachine(kernel, inputs: 1);
            var teacher = new OneclassSupportVectorLearning(svm, inputs)
            {
                Nu = teacherNu,
                Tolerance = teacherTolerance
            };
            double error = teacher.Run();
            Console.WriteLine($"Training complete - error is {error.ToString("#.##")}\t");


            // Test trained classifier.
            Console.Write("Testing outliers:\t");
            foreach (double[] outlier in outliers) {
                WriteResult(svm, outlier);
            }
            Console.WriteLine();
            Console.Write("Testing inliers:\t");
            foreach (double[] inlier in inliers) {
                WriteResult(svm, inlier);
            }
            Console.WriteLine();
            Console.WriteLine();
        }

        private static void WriteResult(KernelSupportVectorMachine svm, double[] coordinate)
        {
            double result = svm.Compute(coordinate);
            if (Math.Sign(result) == 1) {
                Console.Write("I");
            }
            else {
                Console.Write("O");
            }
            Console.Write($" ({result.ToString("#.##")})\t");
        }