新建一個窗口項目。

PM> Install-Package Accord -Version 3.8.0 正在嘗試收集與目標為“.NETFramework,Version=v4.6”的項目“learnai”有關的包“Accord.3.8.0”的依賴項信息 正在嘗試解析程序包“Accord.3.8.0”的依賴項，DependencyBehavior 為“Lowest” 正在解析操作以安裝程序包“Accord.3.8.0” 已解析操作以安裝程序包“Accord.3.8.0”GET https://api.nuget.org/v3-flatcontainer/accord/3.8.0/accord.3.8.0.nupkgOK https://api.nuget.org/v3-flatcontainer/accord/3.8.0/accord.3.8.0.nupkg 84ms 正在安裝 Accord 3.8.0。 正在將程序包“Accord.3.8.0”添加到文件夾“E:\pro\books\AI_.net\src\AI_learn\learnai\packages” 已將程序包“Accord.3.8.0”添加到文件夾“E:\pro\books\AI_.net\src\AI_learn\learnai\packages” 已將程序包“Accord.3.8.0”添加到“packages.config” 已將“Accord 3.8.0”成功安裝到 learnaiPM> Install-Package Accord.MachineLearning PM> Install-Package Accord.Controls

先從C#程序開始，建立一個c#控制臺程序，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 kernelvar smo = new SequentialMinimalOptimization<Gaussian>(){Complexity = 100 // Create a hard-margin SVM };// Use the algorithm to learn the svmvar svm = smo.Learn(inputs, outputs);// Compute the machine's answers for the given inputsbool[] 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.ControlsImports Accord.MachineLearning.VectorMachines.LearningImports Accord.Math.Optimization.LossesImports Accord.StatisticsImports Accord.Statistics.KernelsImports SystemPublic Class Form1'異或運算的SVM分類'定義樣本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 = 01, ' 2. 1 Xor 0 = 11,' 3. 0 Xor 1 = 10' 4. 1 Xor 1 = 0}'創建高斯核的SVM分類Dim smo As New SequentialMinimalOptimization(Of Gaussian)Private Sub Button1_Click(sender As Object, e As EventArgs) Handles Button1.Clicksmo.Complexity = 100Dim 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類的主干定義。

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 是一個 類型形參，即你在聲明此類時提供的數據類型的占位符。 在代碼中的其他地方，可以通過為 classHolder 提供不同的數據類型來聲明不同版本的 t 下面的示例演示了兩個此類聲明。

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

上面的語句聲明了 構造類，在這些類中，特定的類型替換了類型形參。 此類替換會在構造類中的代碼內進行傳播。 下面的示例顯示了 processNewItem 過程在 integerClass中的外觀。

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

總結

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