Accord.Tests.MachineLearning.MulticlassSupportVectorLearningTest.RunTest3 C# (CSharp) Method

        public void RunTest3()
        {

            double[][] inputs =
            {
                // Tickets with the following structure should be assigned to location 0
                new double[] { 1, 4, 2, 0, 1 }, // should be assigned to location 0
                new double[] { 1, 3, 2, 0, 1 }, // should be assigned to location 0

                // Tickets with the following structure should be assigned to location 1
                new double[] { 3, 0, 1, 1, 1 }, // should be assigned to location 1
                new double[] { 3, 0, 1, 0, 1 }, // should be assigned to location 1

                // Tickets with the following structure should be assigned to location 2
                new double[] { 0, 5, 5, 5, 5 }, // should be assigned to location 2
                new double[] { 1, 5, 5, 5, 5 }, // should be assigned to location 2

                // Tickets with the following structure should be assigned to location 3
                new double[] { 1, 0, 0, 0, 0 }, // should be assigned to location 3
                new double[] { 1, 0, 0, 0, 0 }, // should be assigned to location 3
            };

            int[] outputs =
            {
                0, 0, // Those are the locations for the first two vectors above
                1, 1, // Those are the locations for the next two vectors above
                2, 2, // Those are the locations for the next two vectors above
                3, 3, // Those are the locations for the last two vectors above
            };

            // Since this is a simplification, a linear machine will suffice:
            IKernel kernel = new Linear();

            // Create the machine for feature vectors of length 5, for 4 possible locations
            MulticlassSupportVectorMachine machine = new MulticlassSupportVectorMachine(5, kernel, 4);

            // Create a new learning algorithm to train the machine
            MulticlassSupportVectorLearning target = new MulticlassSupportVectorLearning(machine, inputs, outputs);

            // Use the standard SMO algorithm
            target.Algorithm = (svm, classInputs, classOutputs, i, j) =>
                new SequentialMinimalOptimization(svm, classInputs, classOutputs);

            // Train the machines
            double actual = target.Run();


            // Compute the answer for all training samples
            for (int i = 0; i < inputs.Length; i++)
            {
                double[] answersWeights;

                double answer = machine.Compute(inputs[i], MulticlassComputeMethod.Voting, out answersWeights);

                // Assert it has been classified correctly
                Assert.AreEqual(outputs[i], answer);

                // Assert the most probable answer is indeed the correct one
                int imax; Matrix.Max(answersWeights, out imax);
                Assert.AreEqual(answer, imax);
            }
        }