M03 利用Accord 进行机器学习的第一个小例子

 1 using System;
 2 using Accord.Controls;
 3 using Accord.MachineLearning.VectorMachines;
 4 using Accord.MachineLearning.VectorMachines.Learning;
 5 using Accord.Math;
 6 using Accord.Statistics.Kernels;
 7 using Accord.Math.Optimization.Losses;
 8 using Accord.Statistics;
 9 
10 namespace SampleApplication1
11 {
12     class Program
13     {
14         [MTAThread]
15         static void Main(string[] args)
16         {
17             double[][] inputs =
18             {
19                 /* 1.*/ new double[] { 0, 0 },
20                 /* 2.*/ new double[] { 1, 0 }, 
21                 /* 3.*/ new double[] { 0, 1 }, 
22                 /* 4.*/ new double[] { 1, 1 },
23             };
24 
25             int[] outputs =
26             { 
27                 /* 1. 0 xor 0 = 0: */ 0,
28                 /* 2. 1 xor 0 = 1: */ 1,
29                 /* 3. 0 xor 1 = 1: */ 1,
30                 /* 4. 1 xor 1 = 0: */ 0,
31             };
32 
33             // Create the learning algorithm with the chosen kernel
34             var smo = new SequentialMinimalOptimization<Gaussian>()
35             {
36                 Complexity = 100 // Create a hard-margin SVM 
37             };
38 
39             // Use the algorithm to learn the svm
40             var svm = smo.Learn(inputs, outputs);
41 
42             // Compute the machine‘s answers for the given inputs
43             bool[] prediction = svm.Decide(inputs);
44 
45             // Compute the classification error between the expected 
46             // values and the values actually predicted by the machine:
47             double error = new AccuracyLoss(outputs).Loss(prediction);
48 
49             Console.WriteLine("Error: " + error);
50 
51             // Show results on screen 
52             ScatterplotBox.Show("Training data", inputs, outputs);
53             ScatterplotBox.Show("SVM results", inputs, prediction.ToZeroOne());
54 
55             Console.ReadKey();
56         }
57     }
58 }