Asp Core 1.1，DataTable同时存在

Question

我正在开发一个dot net core 1.1 app，我正在尝试使用Accord.Net。根据此页面中的示例（Naive Bayes），我需要将从DB检索到的数据转换为DataTable。

问题是，在使用DataTable时出现了这个错误：

  

'Shim，...'和'中存在'DataTable'类型   'System.Data.Common，...'

即使我使用它：

DataTable learningDataNotCodifiedAsDataTable = new DataTable();

或者这个：

System.Data.DataTable learningDataNotCodifiedAsDataTable = new System.Data.DataTable();

TG

Answer 1

如果你在程序集中有System.Data程序集并且不想删除它或者不能删除它，那么你可以使用extern alias绕过它，但是当我使用它绕过这个错误时我得到{ {1}}错误，如果相信this discussion，原因是：

  

System.Data.DataTable作为空类出现在.Net core（1.0,1.1）中   完成接口实现。这个问题是跟踪的   需要引入API以在.Net中提供类似API的DataTable   芯

仅在.NET Core 2.0中更改，请参阅this SO post。我尝试了.NET Core 2.0项目中的代码（在VS 2017 15.3中），然后才运行正常。

<强>更新 我的意思是这个集会。

但正如你所说，你只有NUGET包，那么你也可以在'DataTable' does not contain a constructor that takes 0/1 arguments文件中使用别名，如下面的Nuget包（我使用csproj你可以在需要的时候用你的Shim包替换它）：

System.Data.Common

然后用C＃引用它：

 <Target Name="DataAlias" BeforeTargets="FindReferenceAssembliesForReferences;ResolveReferences">
    <ItemGroup>
      <ReferencePath Condition="'%(FileName)' == 'System.Data.Common'">
        <Aliases>MyData</Aliases>
      </ReferencePath>
    </ItemGroup>
  </Target>

但是你仍然无法使用，因为你会得到我上面写的构造函数的错误。在这里你有2个选项来解决这个问题：

1. 切换到.NET Core 2.0
2. 如果适合您，请尝试使用extern alias MyData; //1st line in .cs file ... using MyData::System.Data; ... DataTable datatable = new DataTable(); 解决方案from this post

Answer 2

虽然DataTable在.NET Core 1.1中不可用，但现在可以在.NET Core 2.0中使用。如果您可以将项目升级到.NET Core 2.0，那么您将能够在代码中使用它。

但是，如果您现在无法切换到.NET Core 2.0，请注意您不需要将DataTable与Accord.NET框架中的任何方法一起使用。给出或显示它们只是因为它们可以提供一些额外的便利，但它们并不是真正需要的，如下例所示：

string[] columnNames = { "Outlook", "Temperature", "Humidity", "Wind", "PlayTennis" };

string[][] data =
{
    new string[] { "Sunny", "Hot", "High", "Weak", "No" },
    new string[] { "Sunny", "Hot", "High", "Strong", "No" },
    new string[] { "Overcast", "Hot", "High", "Weak", "Yes" },
    new string[] { "Rain", "Mild", "High", "Weak", "Yes" },
    new string[] { "Rain", "Cool", "Normal", "Weak", "Yes" },
    new string[] { "Rain", "Cool", "Normal", "Strong", "No" },
    new string[] { "Overcast", "Cool", "Normal", "Strong", "Yes" },
    new string[] { "Sunny", "Mild", "High", "Weak", "No" },
    new string[] { "Sunny", "Cool", "Normal", "Weak", "Yes" },
    new string[] {  "Rain", "Mild", "Normal", "Weak", "Yes" },
    new string[] {  "Sunny", "Mild", "Normal", "Strong", "Yes" },
    new string[] {  "Overcast", "Mild", "High", "Strong", "Yes" },
    new string[] {  "Overcast", "Hot", "Normal", "Weak", "Yes" },
    new string[] {  "Rain", "Mild", "High", "Strong", "No" },
};

// Create a new codification codebook to
// convert strings into discrete symbols
Codification codebook = new Codification(columnNames, data);

// Extract input and output pairs to train
int[][] symbols = codebook.Transform(data);
int[][] inputs = symbols.Get(null, 0, -1); // Gets all rows, from 0 to the last (but not the last)
int[] outputs = symbols.GetColumn(-1);     // Gets only the last column

// Create a new Naive Bayes learning
var learner = new NaiveBayesLearning();

NaiveBayes nb = learner.Learn(inputs, outputs);

// Consider we would like to know whether one should play tennis at a
// sunny, cool, humid and windy day. Let us first encode this instance
int[] instance = codebook.Translate("Sunny", "Cool", "High", "Strong");

// Let us obtain the numeric output that represents the answer
int c = nb.Decide(instance); // answer will be 0

// Now let us convert the numeric output to an actual "Yes" or "No" answer
string result = codebook.Translate("PlayTennis", c); // answer will be "No"

// We can also extract the probabilities for each possible answer
double[] probs = nb.Probabilities(instance); // { 0.795, 0.205 }