Accord.Tests.MachineLearning.SequentialMinimalOptimizationTest.WeightRatioTest C# (CSharp) Method

        public void WeightRatioTest()
        {
            var dataset = KernelSupportVectorMachineTest.training;
            var inputs = dataset.Submatrix(null, 0, 3);
            var labels = Accord.Math.Tools.Scale(0, 1, -1, 1, dataset.GetColumn(4)).ToInt32();

            Gaussian kernel = Gaussian.Estimate(inputs);

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

                smo.Complexity = 1.0;
                smo.WeightRatio = 10;

                double error = smo.Run();

                Assert.AreEqual(1.0, smo.PositiveWeight);
                Assert.AreEqual(0.1, smo.NegativeWeight);
                Assert.AreEqual(0.7142857142857143, error);
                Assert.AreEqual(265.78327637381551, ((Gaussian)machine.Kernel).Sigma);
                Assert.AreEqual(39, machine.SupportVectors.Length);


                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(12, matrix.TruePositives); // has more importance
                Assert.AreEqual(0, matrix.FalseNegatives); // has more importance
                Assert.AreEqual(30, matrix.FalsePositives);
                Assert.AreEqual(0, matrix.TrueNegatives);

                Assert.AreEqual(1.0, matrix.Sensitivity);
                Assert.AreEqual(0.0, matrix.Specificity);

                Assert.AreEqual(0.44444444444444448, matrix.FScore);
                Assert.AreEqual(0.0, matrix.MatthewsCorrelationCoefficient);
            }

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

                smo.Complexity = 1.0;
                smo.WeightRatio = 0.1;

                double error = smo.Run();

                Assert.AreEqual(0.1, smo.PositiveWeight);
                Assert.AreEqual(1.0, smo.NegativeWeight);
                Assert.AreEqual(0.21428571428571427, error);
                Assert.AreEqual(265.78327637381551, ((Gaussian)machine.Kernel).Sigma);
                Assert.AreEqual(18, machine.SupportVectors.Length);


                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(8, matrix.FalseNegatives);
                Assert.AreEqual(1, matrix.FalsePositives); // has more importance
                Assert.AreEqual(4, matrix.TruePositives);
                Assert.AreEqual(29, matrix.TrueNegatives); // has more importance

                Assert.AreEqual(0.33333333333333331, matrix.Sensitivity);
                Assert.AreEqual(0.96666666666666667, matrix.Specificity);

                Assert.AreEqual(0.47058823529411764, matrix.FScore);
                Assert.AreEqual(0.41849149947774944, matrix.MatthewsCorrelationCoefficient);
            }
        }