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

        public void learn_test()
        {
            #region doc_learn
            // Generate always same random numbers
            Accord.Math.Random.Generator.Seed = 0;

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

            // Sample input data
            double[][] inputs =
            {
                new double[] { 1, 2, 0 },
                new double[] { 6, 2, 3 },
                new double[] { 1, 1, 1 },
                new double[] { 7, 6, 2 },
            };

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


            // Create the multi-class learning algorithm for the machine
            var teacher = new MulticlassSupportVectorLearning<Linear>()
            {
                // Configure the learning algorithm to use SMO to train the
                //  underlying SVMs in each of the binary class subproblems.
                Learner = (param) => new SequentialMinimalOptimization<Linear>()
                {
                    // If you would like to use other kernels, simply replace
                    // the generic parameter to the desired kernel class, such
                    // as for example, Polynomial or Gaussian:

                    Kernel = new Linear() // use the Linear kernel
                }
            };

            // Estimate the multi-class support vector machine using one-vs-one method
            MulticlassSupportVectorMachine<Linear> ovo = teacher.Learn(inputs, outputs);

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

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

            Assert.AreEqual(0, error);
            Assert.IsTrue(predicted.IsEqual(outputs));
            Assert.IsTrue(ovo.Scores(inputs[0]).IsEqual(new double[] { 0.62, -0.25, -0.59, -0.62 }, 1e-2));
            Assert.IsTrue(ovo.Scores(inputs[1]).IsEqual(new double[] { -0.62, -0.57, -0.13, 0.62 }, 1e-2));
            Assert.IsTrue(ovo.Scores(inputs[2]).IsEqual(new double[] { -0.25, 0.63, -0.63, -0.51 }, 1e-2));
        }