Accord.Tests.MachineLearning.MulticlassSupportVectorLearningTest.RunTest C# (CSharp) Method

        public void RunTest()
        {
            Accord.Math.Random.Generator.Seed = 0;

            // Sample data
            //   The following is a simple auto association function
            //   in which each input correspond to its own class. This
            //   problem should be easily solved using a Linear kernel.

            // Sample input data
            double[][] inputs =
            {
                new double[] { 0 },
                new double[] { 3 },
                new double[] { 1 },
                new double[] { 2 },
            };

            // Output for each of the inputs
            int[] outputs = { 0, 3, 1, 2 };


            // Create a new Linear kernel
            IKernel kernel = new Linear();

            // Create a new Multi-class Support Vector Machine for one input,
            //  using the linear kernel and four disjoint classes.
            var machine = new MulticlassSupportVectorMachine(1, kernel, 4);

            // Create the Multi-class learning algorithm for the machine
            var teacher = new MulticlassSupportVectorLearning(machine, inputs, outputs);

            // Configure the learning algorithm to use SMO to train the
            //  underlying SVMs in each of the binary class subproblems.
            teacher.Algorithm = (svm, classInputs, classOutputs, i, j) =>
                new SequentialMinimalOptimization(svm, classInputs, classOutputs);

            // Run the learning algorithm
            double error = teacher.Run();

            Assert.AreEqual(0, error);
            Assert.AreEqual(0, machine.Compute(inputs[0]));
            Assert.AreEqual(3, machine.Compute(inputs[1]));
            Assert.AreEqual(1, machine.Compute(inputs[2]));
            Assert.AreEqual(2, machine.Compute(inputs[3]));
        }