using System;

 using Accord.Controls;

 using Accord.MachineLearning.VectorMachines;

 using Accord.MachineLearning.VectorMachines.Learning;

 using Accord.Math;

 using Accord.Statistics.Kernels;

 using Accord.Math.Optimization.Losses;

 using Accord.Statistics;

 namespace SampleApplication1

 {

     class Program

     {

         [MTAThread]

         static void Main(string[] args)

         {

             double[][] inputs =

             {

                 /* 1.*/ new double[] { ,  },

                 /* 2.*/ new double[] { ,  },

                 /* 3.*/ new double[] { ,  },

                 /* 4.*/ new double[] { ,  },

             };

             int[] outputs =

             {

                 /* 1. 0 xor 0 = 0: */ ,

                 /* 2. 1 xor 0 = 1: */ ,

                 /* 3. 0 xor 1 = 1: */ ,

                 /* 4. 1 xor 1 = 0: */ ,

             };

             // Create the learning algorithm with the chosen kernel

             var smo = new SequentialMinimalOptimization<Gaussian>()

             {

                 Complexity =  // Create a hard-margin SVM

             };

             // Use the algorithm to learn the svm

             var svm = smo.Learn(inputs, outputs);

             // Compute the machine's answers for the given inputs

             bool[] prediction = svm.Decide(inputs);

             // Compute the classification error between the expected

             // values and the values actually predicted by the machine:

             double error = new AccuracyLoss(outputs).Loss(prediction);

             Console.WriteLine("Error: " + error);

             // Show results on screen

             ScatterplotBox.Show("Training data", inputs, outputs);

             ScatterplotBox.Show("SVM results", inputs, prediction.ToZeroOne());

             Console.ReadKey();

         }

     }

 }