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

        public void multiclass_linear_new_usage()
        {
            #region doc_learn_ldcd
            // Let's say we have the following data to be classified
            // into three possible classes. Those are the samples:
            //
            double[][] inputs =
            {
                //               input         output
                new double[] { 0, 1, 1, 0 }, //  0 
                new double[] { 0, 1, 0, 0 }, //  0
                new double[] { 0, 0, 1, 0 }, //  0
                new double[] { 0, 1, 1, 0 }, //  0
                new double[] { 0, 1, 0, 0 }, //  0
                new double[] { 1, 0, 0, 0 }, //  1
                new double[] { 1, 0, 0, 0 }, //  1
                new double[] { 1, 0, 0, 1 }, //  1
                new double[] { 0, 0, 0, 1 }, //  1
                new double[] { 0, 0, 0, 1 }, //  1
                new double[] { 1, 1, 1, 1 }, //  2
                new double[] { 1, 0, 1, 1 }, //  2
                new double[] { 1, 1, 0, 1 }, //  2
                new double[] { 0, 1, 1, 1 }, //  2
                new double[] { 1, 1, 1, 1 }, //  2
            };

            int[] outputs = // those are the class labels
            {
                0, 0, 0, 0, 0,
                1, 1, 1, 1, 1,
                2, 2, 2, 2, 2,
            };

            // Create a one-vs-one multi-class SVM learning algorithm 
            var teacher = new MulticlassSupportVectorLearning<Linear>()
            {
                // using LIBLINEAR's L2-loss SVC dual for each SVM
                Learner = (p) => new LinearDualCoordinateDescent()
                {
                    Loss = Loss.L2
                }
            };

            // Configure parallel execution options
            teacher.ParallelOptions.MaxDegreeOfParallelism = 1;

            // Learn a machine
            var machine = teacher.Learn(inputs, outputs);

            // Obtain class predictions for each sample
            int[] predicted = machine.Decide(inputs);

            // Compute classification error
            double error = new ZeroOneLoss(outputs).Loss(predicted);
            #endregion

            Assert.AreEqual(0, error);
            Assert.IsTrue(predicted.IsEqual(outputs));
        }