Question

编辑-根据史蒂夫（Steve）和马可（Marco）的回复，我已经编辑了代码。在课程中，教授使用了StreamWriter，因此我相信他可能希望我们也这样做。这是修改后的代码：

 using System;
 using System.IO;

 namespace Assignment4
  {
   class Program
   {
      static void Main(string[] args)
      {
        FavoriteNumber();
      }
         static void FavoriteNumber()
        {
         Console.WriteLine("Please input your favorite number: ");

         var result = Console.ReadLine();

         using (StreamWriter writer = new StreamWriter("FavoriteNumber.txt"))
            {
                writer.WriteLine(result);
            }
      }

   }
  }

我将计算机编程基础知识课程作为不相关学位的GenEd要求，这意味着我是对此的新手，并且确实只是想对它们的工作原理有一个非常基本的了解。话虽如此，对于这个论坛上的大多数用户来说，这可能是一个非常简单的问题。此作业的说明如下：

“编写一个使用自定义方法来接受用户输入并将其保存到文本文件的C＃程序。”

有人可以告诉我我编写的代码是否满足这些参数？我真的因这项任务而迷失了，但我希望奇迹能使我做对。预先感谢您的帮助！

using System;
using System.IO;

namespace Assignment4
{
   class Program
   {
      static void Main(string[] args)
      {
      }  

          static void addNumbers(int x, int y)
          {
            int result = x+y;

            using (StreamWriter writer = new StreamWriter("Assignment4.txt"))
            {
               writer.WriteLine(result);  
            }
          }      
   }
}

Answer 1

您需要在某处接受用户输入。您可以使用File.WriteAllText()来做到这一点。它一直等到用户输入内容，然后再继续。另外，您不需要StreamWriter。您可以只使用static void WriteTextToFile() { WriteTextToFile(); } static void WriteTextToFile() { Console.WriteLine("Please enter some value:"); var valueToWrite = Console.ReadLine(); File.WriteAllText("Assignment4.txt", valueToWrite); Console.WriteLine("Thanks alot. Press a key to close."); Console.ReadKey(); }

结果看起来像这样：

data <- read.csv("H:/uni/MS_DS/disease.csv")
data

> data
         radius      texture perimeter   area smoothness desease_rate
1  -0.018743998  0.002521470 -0.005025 0.0710 0.00000000         0.07
2  -0.027940652  0.003164681 -0.004625 0.0706 0.06476967         0.02
3   0.002615946  0.001328688 -0.005525 0.0726 0.06268457         0.07
4   0.041963329  0.002769471 -0.004325 0.0699 0.06013138         0.06
5   0.030261380  0.005725780 -0.003525 0.0695 0.05942403         0.04
6  -0.030559594  0.001576348 -0.002525 0.0695 0.06110087         0.05
7   0.002698690 -0.003028856 -0.006025 0.0706 0.06207810         0.07
8  -0.044996901  0.000617110 -0.009525 0.0691 0.05940039         0.05
9   0.022993350 -0.000637109 -0.015425 0.0695 0.05870643         0.03
10  0.001398530 -0.000470057 -0.017125 0.0705 0.05540871         0.01
11  0.026827990  0.000509490 -0.014025 0.0681 0.05588225         0.06
12 -0.076220726  0.001018820 -0.010225 0.0631 0.05515852         0.01
13 -0.021917789  0.000822517 -0.003925 0.0576 0.05584590         0.03
14  0.012491060 -0.007363090  0.005175 0.0569 0.05120000         0.03
15  0.038281834 -0.008005798  0.014975 0.0576 0.04940000         0.06
16 -0.033198384  0.000350052  0.022875 0.0564 0.04930000         0.01
17 -0.002358179  0.003846831  0.022675 0.0572 0.05050000         0.07
18  0.020808766  0.000536629  0.024575 0.0656 0.04820000         0.04
19  0.091888897 -0.002393641  0.009775 0.0761 0.04740000         0.07
20 -0.036293550 -0.002889337  0.001775 0.0828 0.04770000         0.01

#Multiple Linear Model - fitting the model. 
multilinearmodel = lm(desease_rate ~ radius + texture + perimeter + area +                                 
smoothness, data = df1)
summary(multilinearmodel)

Call:
lm(formula = desease_rate ~ radius + texture + perimeter + area + 
    smoothness, data = df1)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.032172 -0.013960 -0.004256  0.013622  0.033051 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)  
(Intercept)  0.06616    0.06155   1.075   0.3006  
radius       0.33809    0.14270   2.369   0.0327 *
texture      1.16524    1.54157   0.756   0.4623  
perimeter   -0.02464    0.46819  -0.053   0.9588  
area        -0.06218    0.82411  -0.075   0.9409  
smoothness  -0.36014    0.38102  -0.945   0.3606  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.0219 on 14 degrees of freedom
Multiple R-squared:  0.3298,    Adjusted R-squared:  0.09049 
F-statistic: 1.378 on 5 and 14 DF,  p-value: 0.2909

> #Anova test.  
> anova(multilinearmodel)
Analysis of Variance Table

Response: desease_rate
           Df    Sum Sq    Mean Sq F value  Pr(>F)  
radius      1 0.0026031 0.00260313  5.4272 0.03531 *
texture     1 0.0002587 0.00025868  0.5393 0.47484  
perimeter   1 0.0000134 0.00001340  0.0279 0.86964  
area        1 0.0000012 0.00000118  0.0025 0.96109  
smoothness  1 0.0004285 0.00042853  0.8934 0.36058  
Residuals  14 0.0067151 0.00047965                  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> # AIC
> AIC(multilinearmodel)
[1] -89.2251

> # BIC
> BIC(multilinearmodel)
[1] -82.25498

如何使用自定义方法编写将用户输入写入文本文件的C＃程序？

1 个答案: