Accord.Tests.MachineLearning.KernelSupportVectorMachineTest.ComputeTest C# (CSharp) Method

        public void ComputeTest()
        {
            // Example AND problem
            double[][] inputs =
            {
                new double[] { 0, 0 }, // 0 and 0: 0 (label -1)
                new double[] { 0, 1 }, // 0 and 1: 0 (label -1)
                new double[] { 1, 0 }, // 1 and 0: 0 (label -1)
                new double[] { 1, 1 }  // 1 and 1: 1 (label +1)
            };

            // Dichotomy SVM outputs should be given as [-1;+1]
            int[] labels =
            {
                // 0,  0,  0, 1
                  -1, -1, -1, 1
            };

            // Create a Support Vector Machine for the given inputs
            KernelSupportVectorMachine machine = new KernelSupportVectorMachine(new Gaussian(0.1), inputs[0].Length);

            // Instantiate a new learning algorithm for SVMs
            var smo = new SequentialMinimalOptimization(machine, inputs, labels);

            // Set up the learning algorithm
            smo.Complexity = 1.0;

            // Run
            double error = smo.Run();

            Assert.AreEqual(-1, Math.Sign(machine.Compute(inputs[0])));
            Assert.AreEqual(-1, Math.Sign(machine.Compute(inputs[1])));
            Assert.AreEqual(-1, Math.Sign(machine.Compute(inputs[2])));
            Assert.AreEqual(+1, Math.Sign(machine.Compute(inputs[3])));

            Assert.AreEqual(error, 0);

            Assert.AreEqual(-0.6640625, machine.Threshold);
            Assert.AreEqual(1, machine.Weights[0]);
            Assert.AreEqual(-0.34375, machine.Weights[1]);
            Assert.AreEqual(-0.328125, machine.Weights[2]);
            Assert.AreEqual(-0.328125, machine.Weights[3]);
        }