Accord.Tests.MachineLearning.LinearDualCoordinateDescentTest.ComputeTest5 C# (CSharp) Method

        public void ComputeTest5()
        {
            var dataset = SequentialMinimalOptimizationTest.yinyang;

            double[][] inputs = dataset.Submatrix(null, 0, 1).ToJagged();
            int[] labels = dataset.GetColumn(2).ToInt32();

            var kernel = new Polynomial(2, 0);

            {
                var machine = new KernelSupportVectorMachine(kernel, inputs[0].Length);
                var smo = new SequentialMinimalOptimization(machine, inputs, labels);
                smo.UseComplexityHeuristic = true;

                double error = smo.Run();

                Assert.AreEqual(0.2, error);

                Assert.AreEqual(0.11714451552090824, smo.Complexity);

                int[] actual = new int[labels.Length];
                for (int i = 0; i < actual.Length; i++)
                    actual[i] = Math.Sign(machine.Compute(inputs[i]));

                ConfusionMatrix matrix = new ConfusionMatrix(actual, labels);
                Assert.AreEqual(20, matrix.FalseNegatives);
                Assert.AreEqual(0, matrix.FalsePositives);
                Assert.AreEqual(30, matrix.TruePositives);
                Assert.AreEqual(50, matrix.TrueNegatives);
            }

            {
                Accord.Math.Tools.SetupGenerator(0);
                var projection = inputs.Apply(kernel.Transform);
                var machine = new SupportVectorMachine(projection[0].Length);
                var smo = new LinearDualCoordinateDescent(machine, projection, labels);
                smo.UseComplexityHeuristic = true;
                smo.Tolerance = 0.01;

                double error = smo.Run();

                Assert.AreEqual(0.18, error);

                Assert.AreEqual(0.11714451552090821, smo.Complexity, 1e-15);

                int[] actual = new int[labels.Length];
                for (int i = 0; i < actual.Length; i++)
                    actual[i] = Math.Sign(machine.Compute(projection[i]));

                ConfusionMatrix matrix = new ConfusionMatrix(actual, labels);
                Assert.AreEqual(17, matrix.FalseNegatives);
                Assert.AreEqual(1, matrix.FalsePositives);
                Assert.AreEqual(33, matrix.TruePositives);
                Assert.AreEqual(49, matrix.TrueNegatives);
            }

            {
                Accord.Math.Random.Generator.Seed = 0;
                var projection = inputs.Apply(kernel.Transform);
                var machine = new SupportVectorMachine(projection[0].Length);
                var smo = new LinearDualCoordinateDescent(machine, projection, labels);
                smo.UseComplexityHeuristic = true;
                smo.Loss = Loss.L1;

                double error = smo.Run();

                Assert.AreEqual(0.2, error);

                Assert.AreEqual(0.11714451552090821, smo.Complexity, 1e-15);

                int[] actual = new int[labels.Length];
                for (int i = 0; i < actual.Length; i++)
                    actual[i] = Math.Sign(machine.Compute(kernel.Transform(inputs[i])));

                ConfusionMatrix matrix = new ConfusionMatrix(actual, labels);
                Assert.AreEqual(20, matrix.FalseNegatives);
                Assert.AreEqual(0, matrix.FalsePositives);
                Assert.AreEqual(30, matrix.TruePositives);
                Assert.AreEqual(50, matrix.TrueNegatives);
            }
        }