.net随笔- vb.net Accord.Net机器学习库安装与SVM简单分类

新建一个窗口项目。

PM> Install-Package Accord -Version 3.8.0
正在尝试收集与目标为“.NETFramework,Version=v4.6”的项目“learnai”有关的包“Accord.3.8.0”的依赖项信息
正在尝试解析程序包“Accord.3.8.0”的依赖项，DependencyBehavior 为“Lowest”
正在解析操作以安装程序包“Accord.3.8.0”
已解析操作以安装程序包“Accord.3.8.0”
  GET https://api.nuget.org/v3-flatcontainer/accord/3.8.0/accord.3.8.0.nupkg
  OK https://api.nuget.org/v3-flatcontainer/accord/3.8.0/accord.3.8.0.nupkg 84ms
正在安装 Accord 3.8.0。
正在将程序包“Accord.3.8.0”添加到文件夹“E:\pro\books\AI_.net\src\AI_learn\learnai\packages”
已将程序包“Accord.3.8.0”添加到文件夹“E:\pro\books\AI_.net\src\AI_learn\learnai\packages”
已将程序包“Accord.3.8.0”添加到“packages.config”
已将“Accord 3.8.0”成功安装到 learnai
 PM> Install-Package Accord.MachineLearning
PM> Install-Package Accord.Controls

先从C#程序开始，建立一个c#控制台程序，注意引入System.Windows.Forms

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

namespace GettingStarted
{
    class Program
    {
        [MTAThread]
        static void Main(string[] args)
        {
            double[][] inputs =
            {
                /* 1.*/ new double[] { 0, 0 },
                /* 2.*/ new double[] { 1, 0 }, 
                /* 3.*/ new double[] { 0, 1 }, 
                /* 4.*/ new double[] { 1, 1 },
            };

            int[] outputs =
            { 
                /* 1. 0 xor 0 = 0: */ 0,
                /* 2. 1 xor 0 = 1: */ 1,
                /* 3. 0 xor 1 = 1: */ 1,
                /* 4. 1 xor 1 = 0: */ 0,
            };

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

下面是vb.net版本的

    Imports Accord.Controls
    Imports Accord.MachineLearning.VectorMachines.Learning
    Imports Accord.Math.Optimization.Losses
    Imports Accord.Statistics
    Imports Accord.Statistics.Kernels
    Imports System
    
    Public Class Form1
        '异或运算的SVM分类
        '定义样本
        Dim inputs As Double()() =
            {
                    New Double() {0, 0},
                     New Double() {1, 0},
                    New Double() {0, 1},
                     New Double() {1, 1}
             }
        Dim outputs As Integer() =
                {
                     0,' 1. 0 Xor 0 = 0
                     1, ' 2. 1 Xor 0 = 1
                     1,' 3. 0 Xor 1 = 1
                     0' 4. 1 Xor 1 = 0
                }
        '创建高斯核的SVM分类
        Dim smo As New SequentialMinimalOptimization(Of Gaussian)
    
        Private Sub Button1_Click(sender As Object, e As EventArgs) Handles Button1.Click
            smo.Complexity = 100
            Dim svm = smo.Learn(inputs, outputs)
            Dim prediction As Boolean() = svm.Decide(inputs)
            Dim trainError As Double = New AccuracyLoss(outputs).Loss(prediction)
    
            Label1.Text = "Error: " + Str(trainError)
            ScatterplotBox.Show("Training data", inputs, outputs)
            ScatterplotBox.Show("SVM results", inputs, prediction.ToZeroOne())
        End Sub

下面的示例演示了泛型类的主干定义。

Public Class classHolder(Of t)
    Public Sub processNewItem(ByVal newItem As t)
        Dim tempItem As t
        ' Insert code that processes an item of data type t.
    End Sub
End Class

在上面的主干中， t 是一个 类型形参，即你在声明此类时提供的数据类型的占位符。 在代码中的其他地方，可以通过为 classHolder 提供不同的数据类型来声明不同版本的 t 下面的示例演示了两个此类声明。

Public integerClass As New classHolder(Of Integer)
Friend stringClass As New classHolder(Of String)

上面的语句声明了 构造类，在这些类中，特定的类型替换了类型形参。 此类替换会在构造类中的代码内进行传播。 下面的示例显示了 processNewItem 过程在 integerClass中的外观。

Public Sub processNewItem(ByVal newItem As Integer)
    Dim tempItem As Integer
    ' Inserted code now processes an Integer item.
End Sub