例項一：訓練SVM，解決XOR分類問題

異或問題的原理：相同為真，不同為假。

需要使用的包：Accord.MachineLearning、Accord.Controls、Accord.Math、Accord.Statistics。

程式碼如下：C#、控制檯應用程式。

using Accord.Controls;
using Accord.MachineLearning.VectorMachines.Learning;
using Accord.Math.Optimization.Losses;
using Accord.Statistics;
using Accord.Statistics.Kernels;
using System;

namespace Support_Vector_Machines
{
    class Program
    {
        static void Main(string[] args)
        {
            double[][] inputs =
            {
                new double[] { 0, 0 },
                new double[] { 1, 0 },
                new double[] { 0, 1 },
                new double[] { 1, 1 },
            };
            int[] outputs =
            {
                0,
                1,
                1,
                0,
            };

            //Create the learning algorithm with the chose kernel
            var smo = new SequentialMinimalOptimization<Gaussian>()
            {
                Complexity = 100
            };

            //Use the algorithm to learn the svm
            var svm = smo.Learn(inputs, outputs);

            //Compute the machina`s answer 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();
        }
    }
}
 

結果如下：