List<Pojo>按pojo对象的属性排序

本文介绍如何创建并使用自定义排序类,通过MyComparator类实现特定的排序逻辑,并展示了其在排序操作中的实际应用。

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MyComparator.java代码如下:(pojo类为InfeNumDTO)

package com.zhh.util;  
  
import java.util.Comparator;

import com.brief.pojo.InfeNumDTO;
 
 /*
  * author zhh
  */
public class MyComparator implements Comparator {  
  
    public int compare(Object o1, Object o2) {  
        InfeNumDTO ind1 = (InfeNumDTO)o1;  
        InfeNumDTO ind2 = (InfeNumDTO)o2;  
        if(ind1.getInfeNum() < ind2.getInfeNum()){//通过这里设定排列顺序  
            return 1;  
        }else{  
            return 0;  
        }  
    }  
} 


用法:

Comparator  comp = new MyComparator();  

             Collections.sort(abTypeInfeList,comp);


可以通过反射,将该排序类写得更通用。

package com.xuetu.service.impl; import com.xuetu.mapper.LearningResourceMapper; import com.xuetu.pojo.LearningResource; import com.xuetu.pojo.Student; import com.xuetu.pojo.Subject; import org.springframework.beans.factory.annotation.Autowired; import org.springframework.stereotype.Service; import java.util.*; @Service public class AStarPathServiceImpl { @Autowired LearningResourceMapper learningResourceMapper; private class Node implements Comparable<Node> { LearningResource resource; Node parent; double g; // 已花费代价 double h; // 启发式代价 double f() { return g + h; } public int compareTo(Node other) { return Double.compare(this.f(), other.f()); } } public List<LearningResource> planPath(Student student, List<LearningResource> resources, Subject subject) { PriorityQueue<Node> openList = new PriorityQueue<>(); Map<LearningResource, Double> costSoFar = new HashMap<>(); // 初始化开放列表 Node start = new Node(); start.resource = getStartResource(student, subject); start.g = 0; start.h = heuristic(start.resource, student, subject); openList.add(start); while (!openList.isEmpty()) { Node current = openList.poll(); if (isGoalReached(current, student)) { return reconstructPath(current); } for (LearningResource neighbor : getNeighbors(current.resource)) { double newCost = current.g + computeCost(current.resource, neighbor, student); if (!costSoFar.containsKey(neighbor) || newCost < costSoFar.get(neighbor)) { costSoFar.put(neighbor, newCost); Node next = new Node(); next.resource = neighbor; next.parent = current; next.g = newCost; next.h = heuristic(neighbor, student, subject); openList.add(next); } } } return Collections.emptyList(); } private LearningResource[] getNeighbors(LearningResource resource) { } //从目标节点回溯到起始节点,构建学习路径 private List<LearningResource> reconstructPath(Node current) { List<LearningResource> path = new ArrayList<>(); while (current != null) { path.add(current.resource); current = current.parent; } // 由于我们是从终点回溯到起点,所以需要反转路径列表 Collections.reverse(path); return path; } //判断是否到达终点 private boolean isGoalReached(Node current, Student student) { } //获取开始学科资源 private LearningResource getStartResource(Student student, Subject subject) { } // 代价计算(核心业务逻辑) private double computeCost(LearningResource from, LearningResource to, Student student) { // 考虑因素:难度匹配度、兴趣匹配度、学习时间 double difficultyCost = Math.abs(to.getDifficulty() - student.getAbilityLevel().get(to.getSubject().getName())); double interestWeight = student.getInterests().contains(to.getSubject().getName()) ? 0.5 : 1.2; double timeCost = to.getDuration() / 60.0; // 转换为小时 return difficultyCost * interestWeight + timeCost; } // 启发式函数 private double heuristic(LearningResource resource, Student student, Subject target) { // 综合考虑兴趣匹配度、能力差距、跨学科关联 double interestMatch = calculateInterestSimilarity(resource.getInterestTags(), student.getInterests()); double abilityGap = Math.abs(resource.getDifficulty() - student.getAbilityLevel().get(target.getName())); double crossSubjectBonus = calculateCrossSubjectBonus(resource, student); return (abilityGap * 0.6) - (interestMatch * 0.3) - (crossSubjectBonus * 0.1); } //计算兴趣匹配度 private double calculateInterestSimilarity(Object interestTags, Collection<String> interests) { } //计算跨学科关联 private double calculateCrossSubjectBonus(LearningResource resource, Student student) { }上面项目是一个基于AStar算法的学习路径规划算法请完善上面代码并给出详细解释
05-13
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