您想向您的朋友发送包含不同物品的包裹。放入包装中的每件物品都具有诸如索引号，重量和成本之类的参数。包装有重量限制。您的目标是确定要放入包装中的物品，以使总重量小于或等于包装限制，并且总成本尽可能大。
如果有多个相同价格的包裹，您最好发送重量较轻的包裹。
输入样本：
您的程序应将文件名路径作为其第一个参数。输入文件包含几行。每行是一个测试用例。
每行包含包装可以承受的重量（在冒号之前）和需要选择的内容列表。每个物品都用括号括起来，第一个数字是物品的索引号，第二个数字是重量，第三个数字是成本。例如：
81 : (1,53.38,45)(2,88.62,98) (3,78.48,3)(4,72.30,76) (5,30.18,9)(6,46.34,48)
8 : (1,15.3,34)
75:(1,85.31,29) (2,14.55,74)(3,3.98,16) (4,26.24,55)(5,63.69,52) (6,76.25,75)(7,60.02,74) (8,93.18,35)(9,89.95,78)
56 : (1,90.72,13)(2,33.80,40) (3,43.15,10)(4,37.97,16) (5,46.81,36)(6,48.77,79) (7,81.80,45)(8,19.36,79) (9,6.76,$64)
输出样本：
对于您放入包装中的每组物品，请提供一个列表（物品的索引号以逗号分隔）。
例如。
4 //for package1
- //for package2
2,7 //for package3
8,9 //for package4
约束：
一个包裹可以承受的最大重量≤100。您可能需要从最大重量中选择最多15件物品，而一件物品的成本≤100
是否有比https://discuss.codechef.com/questions/104934/package-problemin-java更优雅，更容易理解的解决方案
最佳答案
下面是代码（KnapSack）：
public class FriendPackageKnapsack {
public static void parserThings(String[] sub2, List<Package> things,
double maxWeight) {
for (int i = 0; i < sub2.length; i++) {
String[] sub3 = (sub2[i].substring(1, sub2[i].length() - 1)).split(",");
int id = Integer.parseInt(sub3[0]);
double weight = Double.parseDouble(sub3[1]);
double cost = Double.parseDouble(sub3[2].substring(1, sub3[2].length()));
if (weight <= maxWeight) {
Package condidat = new Package(id, weight, cost);
things.add(condidat);
}
}
}
public static String getOptimumFor(List<Package> things, int r, int maxWt) {
int indexSolution = 0;
String returnData = "";
double maxWeight = 0;
double maxCost = 0;
int[] data = new int[r];
List<Integer> res = new ArrayList<Integer>();
int[] arr = new int[things.size()];
for (int i = 0; i < things.size(); i++) {
arr[i] = i;
}
getCombination(arr, data, res, 0, 0);
for (int i = 0; i <= res.size() - r; i += r) {
double someWeight = 0;
double someCost = 0;
for (int j = 0; j < r; j++) {
someWeight += things.get(res.get(i + j)).getWeight();
someCost += things.get(res.get(i + j)).getCost();
}
if (someWeight <= maxWt) {
if ((someCost > maxCost)
|| ((someCost == maxCost) && (someWeight <= maxWeight))) {
indexSolution = i;
maxWeight = someWeight;
maxCost = someCost;
}
}
}
for (int k = indexSolution; k < r + indexSolution; k++) {
returnData += res.get(k) + ",";
}
return returnData + maxCost + "," + maxWeight;
}
public static void getCombination(int[] arr, int[] data, List<Integer> res, int start,
int index) {
if (index == data.length) {
for (int j = 0; j < data.length; j++) { res.add(data[j]); }
return;
}
for (int i = start; i < arr.length && arr.length - i >= data.length - index; i++) {
data[index] = arr[i];
getCombination(arr, data, res, i + 1, index + 1);
}
}
public static void main(String[] args) {
File file = new File("packageproblem.txt");
BufferedReader in = null;
try {
in = new BufferedReader(new FileReader(file));
}
catch (FileNotFoundException e) { e.printStackTrace(); }
String line = "";
List<Package> things = new ArrayList<>();
try {
while ((line = in.readLine()) != null) {
String s = "";
// Parsing line
String[] sub1 = line.split(" : ");
int N = Integer.parseInt(sub1[0]);
String[] sub2 = sub1[1].split(" ");
if (sub2.length > 1) {
things.clear();
parserThings(sub2, things, N);
double maxCost = 0;
double maxWeight = 0;
for (int i = 1; i <= things.size(); i++) {
String resultat = getOptimumFor(things, i, N);
// System.out.println(resultat);
String[] sub4 = resultat.split(",");
double cost = Double.parseDouble(sub4[sub4.length - 2]);
double weight = Double.parseDouble(sub4[sub4.length - 1]);
if (cost == maxCost) {
if (weight < maxWeight) {
maxCost = cost;
maxWeight = weight;
s = resultat;
}
}
if (cost > maxCost) {
maxCost = cost;
maxWeight = weight;
s = resultat;
}
}
// System.out.println(s);
String[] sub5 = s.split(",");
String ss = "";
for (int i = 0; i < sub5.length - 2; i++) {
ss += things.get(Integer.parseInt(sub5[i])).getId() + ",";
}
if (ss.equals("")) { System.out.println("-"); }
else { System.out.println(ss.substring(0, ss.length() - 1)); }
}
else { System.out.println("-"); }
}
}
catch (IOException e) { e.printStackTrace(); }
}
}
class Package {
private int id;
private double weight;
private double cost;
public Package(int id, double weight, double cost) {
this.id = id;
this.weight = weight;
this.cost = cost;
}
public int getId() { return id; }
public void setId(int id) { this.id = id; }
public double getWeight() { return weight; }
public void setWeight(double weight) { this.weight = weight; }
public double getCost() { return cost; }
public void setCost(double cost) { this.cost = cost; }
}
该代码取自here。
关于java - 代码测试-在给定重量的包装下最大化成本，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/49825103/