我想从一个集合中选择一个随机项目，但是选择任何项目的机会应该与相关权重成比例
示例输入：
item weight
---- ------
sword of misery 10
shield of happy 5
potion of dying 6
triple-edged sword 1
所以，如果我有4个可能的项目，那么获得任何一个没有权重的项目的机会将是1/4。
在这种情况下，使用者获得痛苦之剑的可能性应该是三角剑的10倍。
如何在Java中进行加权随机选择？
答案 0 :(得分：98)
我会使用NavigableMap
public class RandomCollection<E> {
private final NavigableMap<Double, E> map = new TreeMap<Double, E>();
private final Random random;
private double total = 0;
public RandomCollection() {
this(new Random());
}
public RandomCollection(Random random) {
this.random = random;
}
public RandomCollection<E> add(double weight, E result) {
if (weight <= 0) return this;
total += weight;
map.put(total, result);
return this;
}
public E next() {
double value = random.nextDouble() * total;
return map.higherEntry(value).getValue();
}
}
说我有动物狗，猫，马的列表，概率分别为40％，35％，25％
RandomCollection<String> rc = new RandomCollection<>()
.add(40, "dog").add(35, "cat").add(25, "horse");
for (int i = 0; i < 10; i++) {
System.out.println(rc.next());
}
答案 1 :(得分：23)
您将找不到此类问题的框架，因为所请求的功能仅仅是一个简单的功能。做这样的事情：
interface Item {
double getWeight();
}
class RandomItemChooser {
public Item chooseOnWeight(List<Item> items) {
double completeWeight = 0.0;
for (Item item : items)
completeWeight += item.getWeight();
double r = Math.random() * completeWeight;
double countWeight = 0.0;
for (Item item : items) {
countWeight += item.getWeight();
if (countWeight >= r)
return item;
}
throw new RuntimeException("Should never be shown.");
}
}
答案 2 :(得分：16)
现在在Apache Commons中有一个类：EnumeratedDistribution
Item selectedItem = new EnumeratedDistribution(itemWeights).sample();
其中itemWeights是List<Pair<Item,Double>>，就像（在Arne的回答中假设Item接口）：
List<Pair<Item,Double>> itemWeights = Collections.newArrayList();
for (Item i : itemSet) {
itemWeights.add(new Pair(i, i.getWeight()));
}
或Java 8：
itemSet.stream().map(i -> new Pair(i, i.getWeight())).collect(toList());
注意：Pair此处必须为org.apache.commons.math3.util.Pair，而不是org.apache.commons.lang3.tuple.Pair。
答案 3 :(得分：4)
如果您要滚动很多次（如在游戏中），则应使用别名方法。
下面的代码实际上是这种别名方法的相当长的实现。但这是因为初始化部分。元素的检索速度非常快（请参阅next和applyAsInt方法，它们不会循环播放。
Set<Item> items = ... ;
ToDoubleFunction<Item> weighter = ... ;
Random random = new Random();
RandomSelector<T> selector = RandomSelector.weighted(items, weighter);
Item drop = selector.next(random);
此实施：
Random以获得最佳性能，使用ThreadLocalRandom？）; 无论如何，这里是代码。 （请注意I maintain an up to date version of this class。）
import static java.util.Objects.requireNonNull;
import java.util.*;
import java.util.function.*;
public final class RandomSelector<T> {
public static <T> RandomSelector<T> weighted(Set<T> elements, ToDoubleFunction<? super T> weighter)
throws IllegalArgumentException {
requireNonNull(elements, "elements must not be null");
requireNonNull(weighter, "weighter must not be null");
if (elements.isEmpty()) { throw new IllegalArgumentException("elements must not be empty"); }
// Array is faster than anything. Use that.
int size = elements.size();
T[] elementArray = elements.toArray((T[]) new Object[size]);
double totalWeight = 0d;
double[] discreteProbabilities = new double[size];
// Retrieve the probabilities
for (int i = 0; i < size; i++) {
double weight = weighter.applyAsDouble(elementArray[i]);
if (weight < 0.0d) { throw new IllegalArgumentException("weighter may not return a negative number"); }
discreteProbabilities[i] = weight;
totalWeight += weight;
}
if (totalWeight == 0.0d) { throw new IllegalArgumentException("the total weight of elements must be greater than 0"); }
// Normalize the probabilities
for (int i = 0; i < size; i++) {
discreteProbabilities[i] /= totalWeight;
}
return new RandomSelector<>(elementArray, new RandomWeightedSelection(discreteProbabilities));
}
private final T[] elements;
private final ToIntFunction<Random> selection;
private RandomSelector(T[] elements, ToIntFunction<Random> selection) {
this.elements = elements;
this.selection = selection;
}
public T next(Random random) {
return elements[selection.applyAsInt(random)];
}
private static class RandomWeightedSelection implements ToIntFunction<Random> {
// Alias method implementation O(1)
// using Vose's algorithm to initialize O(n)
private final double[] probabilities;
private final int[] alias;
RandomWeightedSelection(double[] probabilities) {
int size = probabilities.length;
double average = 1.0d / size;
int[] small = new int[size];
int smallSize = 0;
int[] large = new int[size];
int largeSize = 0;
// Describe a column as either small (below average) or large (above average).
for (int i = 0; i < size; i++) {
if (probabilities[i] < average) {
small[smallSize++] = i;
} else {
large[largeSize++] = i;
}
}
// For each column, saturate a small probability to average with a large probability.
while (largeSize != 0 && smallSize != 0) {
int less = small[--smallSize];
int more = large[--largeSize];
probabilities[less] = probabilities[less] * size;
alias[less] = more;
probabilities[more] += probabilities[less] - average;
if (probabilities[more] < average) {
small[smallSize++] = more;
} else {
large[largeSize++] = more;
}
}
// Flush unused columns.
while (smallSize != 0) {
probabilities[small[--smallSize]] = 1.0d;
}
while (largeSize != 0) {
probabilities[large[--largeSize]] = 1.0d;
}
}
@Override public int applyAsInt(Random random) {
// Call random once to decide which column will be used.
int column = random.nextInt(probabilities.length);
// Call random a second time to decide which will be used: the column or the alias.
if (random.nextDouble() < probabilities[column]) {
return column;
} else {
return alias[column];
}
}
}
}
答案 4 :(得分：1)
public class RandomCollection<E> {
private final NavigableMap<Double, E> map = new TreeMap<Double, E>();
private double total = 0;
public void add(double weight, E result) {
if (weight <= 0 || map.containsValue(result))
return;
total += weight;
map.put(total, result);
}
public E next() {
double value = ThreadLocalRandom.current().nextDouble() * total;
return map.ceilingEntry(value).getValue();
}
}
答案 5 :(得分：1)
如果您在选择后需要删除元素，则可以使用其他解决方案。将所有元素添加到'LinkedList'中，每个元素必须按重量添加多次，然后使用Collections.shuffle()，根据JavaDoc
使用默认的随机源随机置换指定的列表。所有排列都以大致相等的可能性发生。
最后，使用pop()或removeFirst()
Map<String, Integer> map = new HashMap<String, Integer>() {{
put("Five", 5);
put("Four", 4);
put("Three", 3);
put("Two", 2);
put("One", 1);
}};
LinkedList<String> list = new LinkedList<>();
for (Map.Entry<String, Integer> entry : map.entrySet()) {
for (int i = 0; i < entry.getValue(); i++) {
list.add(entry.getKey());
}
}
Collections.shuffle(list);
int size = list.size();
for (int i = 0; i < size; i++) {
System.out.println(list.pop());
}
答案 6 :(得分：0)
139
有一个简单的算法可以随机选择一件物品，其中物品具有各自的权重：
计算所有权重之和
选择一个0或更大且小于权重之和的随机数
一次浏览一个项目，从您的随机数中减去它们的权重，直到获得随机数小于该项目权重的项目