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

        private static void testWeights(double[][] inputs, int[] labels, IKernel kernel)
        {
            {
                var machine = new KernelSupportVectorMachine(kernel, inputs[0].Length);
                var smo = new SequentialMinimalOptimization(machine, inputs, labels);

                smo.PositiveWeight = 100;
                smo.NegativeWeight = 1;

                double error = smo.Run();

                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(50, matrix.TruePositives); // has more importance
                Assert.AreEqual(0, matrix.FalseNegatives); // has more importance
            }

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

                smo.PositiveWeight = 1;
                smo.NegativeWeight = 100;

                double error = smo.Run();

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

                var matrix = new ConfusionMatrix(actual, labels);
                Assert.AreEqual(50, matrix.TrueNegatives); // has more importance
                Assert.AreEqual(0, matrix.FalsePositives);  // has more importance
            }
        }