java - 对两个对应的数组进行排序时出错

Question

我正在尝试制作一个概率结果模拟器，其中的信息取自 .csv 文件。创建两个 ArrayList，然后将信息和结果放入这些 ArrayList。 Double ArrayList 包含 String ArrayList 中每个对应的两个团队的置信度评级。

Example: 
Double ArrayList:  25, 22, 50
String ArrayList: Atlanta, Michigan, NY, Detroit

Atlanta and Michigan would correspond to 25, NY and Detroit would correspond to 22.

我已经制作了程序，信心评级双 ArrayList 得到排序，但团队 String ArrayList 没有。这是排序前的两个 ArrayList:

[1.0, 7.0, 8.0, 1.0, 10.0, 2.0, 4.0, 3.0, 1.0, 1.0, 9.0, 1.0, 3.0, 6.0, 0.0, 16.0]
[Green Bay, [Detroit, NY Jets, [NY Giants, [St. Louis, Arizona, [Tampa Bay, Atlanta, [Minnesota, Seattle, Houston, [Buffalo, [Miami, Baltimore, Cincinnati, [Cleveland, Jacksonville, [Tennessee, SF, [Chicago, Denver, [San Diego, KC, [Oakland, Carolina, [New Orleans, [New England, Philly, [Pittsburgh, Indy, [Washington, Dallas]

两个列表整理好之后是这样的:

[16.0, 10.0, 9.0, 8.0, 7.0, 6.0, 4.0, 3.0, 3.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0]
[[NY Giants, [Cleveland, Jacksonville, Seattle, [Cleveland, [St. Louis, Houston, Arizona, [Buffalo, [NY Giants, [Cleveland, [Cleveland, Baltimore, [Miami, Atlanta, [NY Giants, Atlanta, [Tennessee, SF, [Chicago, Denver, [San Diego, KC, [Oakland, Carolina, [New Orleans, [New England, Philly, [Pittsburgh, Indy, [Washington, Dallas]

置信度评级成功地按照降序进行了相应排序，但团队并不对应于各自的评级。事实上，同一个团队被复制了多次。我该如何解决这个问题并让我所有的团队对应于他们适当的评级？ (sortArrays() 方法是排序操作发生的地方)。

import javafx.application.Application;
import javafx.scene.Scene;
import javafx.stage.Stage;
import javafx.scene.layout.*;
import javafx.scene.control.*;
import javafx.stage.FileChooser;
import javafx.geometry.*;
import java.util.*;
import java.io.*;

public class POS extends Application
{
   private ArrayList<Double> confidenceList = new ArrayList<>();
   private ArrayList<String> cityList = new ArrayList<>();
   private BorderPane pane = new BorderPane();
   private Button runBtn = new Button("Run");
   @Override
   public void start(Stage stage)
   {


      VBox vBox = new VBox(20);
      vBox.setPadding(new Insets(15));
      Button selectBtn = new Button("Select File");
      selectBtn.setStyle("-fx-font: 22 arial; -fx-base: #b6e7c9;");
      vBox.getChildren().add(selectBtn);

      selectBtn.setOnAction(e->
      {
         FileChooser fileChooser = new FileChooser();
         fileChooser.setTitle("Open Resource File");
         FileChooser.ExtensionFilter extFilter = 
                        new FileChooser.ExtensionFilter("TEXT files (*.csv)", "*.CSV", ".xlsv", ".XLSV");
                fileChooser.getExtensionFilters().add(extFilter);
         File file = fileChooser.showOpenDialog(stage);

            run(file);


      });

      RadioButton weekBtn = new RadioButton("Current Week");  
      RadioButton seasonBtn = new RadioButton("Entire Season");

      runBtn.setStyle("-fx-font: 22 arial; -fx-base: #b6e7c9;");


      weekBtn.setSelected(true);
      seasonBtn.setDisable(true);
      vBox.getChildren().add(weekBtn);
      vBox.getChildren().add(seasonBtn);
      vBox.getChildren().add(runBtn);

      pane.setLeft(vBox);
      Scene scene = new Scene(pane, 500, 200);
      stage.setScene(scene);
      stage.setTitle("POS");
      stage.show();
   }
   public void run(File file)
   {
      runBtn.setOnAction(e->
      {
         try
         {
            Scanner input = new Scanner(file);
            input.nextLine(); 
            sortFile(file, input);

            input.close();
         }

         catch (InputMismatchException ex)
         {
            System.out.println("Error you seem to have typed the wrong type of file");
         }
         catch(IOException ex)
         {
            System.out.println("Error, file could not be found");
         }


      });
   }
   public void sortFile(File file, Scanner input)
   {
      if (!input.hasNext())
      {
         sortArrays(confidenceList, cityList);
      }
      else
      {
      String strList = Arrays.toString(input.nextLine().split("\t"));
      String[] arrList = strList.split(",");

      int homeRank = Integer.parseInt(arrList[1]);

      int roadRank = Integer.parseInt(arrList[6]);
      Random r = new Random();

      int lowestTeamRank = Math.abs(homeRank - roadRank);

      double numForHomeTeam = 0;
      double numForRoadTeam = 0;
      if (homeRank < roadRank)
      {
         numForHomeTeam = ((r.nextInt(lowestTeamRank) - r.nextInt(2)) + (getLastGameOutcome(arrList[4])* r.nextInt(3))) - getWinPct(arrList[2], arrList[3]);

         numForRoadTeam = ((r.nextInt(roadRank) + r.nextInt(2)) + (getLastGameOutcome(arrList[9])* r.nextInt(3))) - getWinPct(arrList[7], arrList[8]);
      }

      else if (homeRank > roadRank)
      {
         numForHomeTeam = ((r.nextInt(homeRank) - r.nextInt(2)) + (getLastGameOutcome(arrList[4])* r.nextInt(3))) - getWinPct(arrList[2], arrList[3]);

         numForRoadTeam = r.nextInt(lowestTeamRank) - r.nextInt(2) + getLastGameOutcome(arrList[9])* r.nextInt(3) - getWinPct(arrList[7], arrList[8]);
      }



      double confidenceRate = Math.round(Math.abs(numForHomeTeam - numForRoadTeam));
      confidenceList.add(confidenceRate);
      if (numForHomeTeam < numForRoadTeam)
      {
          cityList.add(arrList[0]);
          cityList.add(arrList[5]);
      }
      else if (numForHomeTeam > numForRoadTeam)
      {
         cityList.add(arrList[5]);
         cityList.add(arrList[0]);
      }
      else
      {
         cityList.add(arrList[0]);
         cityList.add(arrList[5]);
      }

      sortFile(file, input);
      }
   }

   public int getLastGameOutcome(String lastGame)
   {
      if (lastGame.charAt(0) == 'W')
      {
         return (int)(Math.random() * 3);
      }

      else
      {
         return (int)(Math.random() * -3);
      }  
   }

   public double getWinPct(String wins, String losses)
   {
       double newWins = Double.parseDouble(wins);
       double newLosses = Double.parseDouble(losses);
       return newWins / (newWins + newLosses);
   } 

   public void sortArrays(ArrayList<Double> doubleArray, ArrayList<String> stringArray)
   {
      System.out.println(doubleArray);
      System.out.println(stringArray);
      for (int i = 0; i < doubleArray.size(); i++)
      {
         for (int j = 0; j < doubleArray.size(); j++)
         {
            if (doubleArray.get(j).compareTo(doubleArray.get(i)) < 1)
            {
               double tempDouble = doubleArray.get(j);
               doubleArray.set(j, doubleArray.get(i));
               doubleArray.set(i, tempDouble);

               String tempString = stringArray.get(j);
               String tempString2 = stringArray.get(j + 1);
               stringArray.set(j, stringArray.get(i));
               stringArray.set(j + 1, stringArray.get(i + 1));
               stringArray.set(i, tempString);
               stringArray.set(i + 1, tempString2);
            }
         }
      }

      System.out.println(doubleArray);
      System.out.println(stringArray);
   } 

}

Answer 1

我不会使用两个独立的数据结构，而是将它们组合成一个表示数据潜在含义的简单 Java 对象的单个列表。

例如，保持信心评级和团队:

public class TeamConfidence implements Comparable<TeamConfidence> {
  private String team;
  private double confidence;

  public TeamConfidence(String team, double confidence) {
    this.team = team;
    this.confidence = confidence;
  }

  @Override
  public int compareTo(TeamConfidence other) {
      if(other == this) { 
        return true;
      } else if (other == null ) {
        return false;
      } else {
        return Double.compare(confidence, other.confidence);
      }
  }

  // include getters and setters, maybe a constructor
}

由于它实现了 Comparable 接口(interface)，您可以使用 Collections.sort 调用来按置信度排序:

List<TeamConfidence> teams = new ArrayList<>();
// populate list
Collections.sort(teams);
// list is now ordered by confidence, and still retains the relation between 
// the team name and the confidence level

---

下面是我们将如何实现这一点的简化示例。

假设我们有一个最小的数据集，使用您的原始示例，将丹佛和巴尔的摩作为一些较低的异常值:

| Team      | Confidence |
| Atlanta   | 25         |
| Michigan  | 25         |
| Detroit   | 22         |
| NY        | 22         |
| Denver    | 13         |
| Baltimore | 1          |

请注意，出于演示目的，我向上面的 TeamConfidence 类添加了一个构造函数。

我们首先要为自己创建一组 TeamConfidence 对象。我们将在此处手动创建它们作为示例，但这是您可以调整以从文件、数据库或其他数据源读取的那种东西。

我们还会将对象添加到 List 中。

// declare a list to hold the TeamConfidence objects
List<TeamConfidence> teams = new ArrayList<>();

// populate the list
teams.add(new TeamConfidence("Detroit", 22.0));
teams.add(new TeamConfidence("Atlanta", 25.0));
teams.add(new TeamConfidence("Baltimore", 1.0));
teams.add(new TeamConfidence("Michigan", 25.0));
teams.add(new TeamConfidence("NY", 22.0));
teams.add(new TeamConfidence("Denver", 13.0));

此时，我们有一个团队列表。现在我们调用 sort:

Collections.sort(teams);

现在我们的名单上有我们的团队。根据您在 TeamConfidence 中实现 compareTo 方法的方式，这将导致先变小，或先变大。 (要交换顺序，乘以 -1；例如 -1*Double.compare(confidence, other.confidence);)

假设这个 compare-to 实现为较小优先，我认为是这样(但我想不起来)，我们的列表将按如下顺序排列:

[(Baltimore, 1.0), (Denver, 13.0), (NY, 22.0), (Detroit, 22.0), (Atlanta, 25.0), (Michigan, 25.0)]

请注意，由于我们的 compareTo 方法仅考虑置信度，因此置信度内没有排序；所以纽约和底特律将相邻，但不能保证纽约始终排在底特律之前。

---

根据下面的评论，最好的模型如下:

public class TeamConfidence implements Comparable<TeamConfidence> {
  private String winner;
  private String loser;
  private double confidence;

  public TeamConfidence(String winner, String loser, double confidence) {
    this.winner = winner;
    this.loser = loser;
    this.confidence = confidence;
  }

  @Override
  public String toString() {
    return "(" + confidence + ", " + winner + ", " + loser ")";
  }

  @Override
  public int compareTo(TeamConfidence other) {
      if(other == this) { 
        return true;
      } else if (other == null ) {
        return false;
      } else {
        return Double.compare(confidence, other.confidence);
      }
  }
}

现在，当您按置信度排序时，列表中的每个元素都将指示赢家和输家。

数据:

| Winner  | Loser     | Confidence |
| Atlanta | Michigan  | 25         |
| NY      | Detroit   | 22         |
| Denver  | Baltimore | 13         |

代码:

List<TeamConfidence> teams = new ArrayList<>();

// populate the list
teams.add(new TeamConfidence("Atlanta", "Michigan", 25.0));
teams.add(new TeamConfidence("NY", "Detroit", 22.0));
teams.add(new TeamConfidence("Denver", "Baltimore", 13.0));

Collections.sort(teams);

结果:

// (confidence, winner, loser)
[(13.0, Denver, Baltimore), (22.0, NY, Detroit), (25.0, Atlanta, Michigan)]