Java中使用TreeMap权重随机算法,以及验证与分析
权重下随机,就是给定各个值不同的权重,再根据权重的比例随机选出一个值
1 /** 2 * Created by Jungle on 2020/2/23. 3 * 4 * @author JungleZhang 5 * @version 1.0.0 6 * @Description 权重下随机的算法 7 */ 8 public class WeightRandom<K, V extends Number> { 9 private TreeMap<Double, K> weightMap = new TreeMap<>(); 10 11 public WeightRandom(@NotNull List<Pair<K, V>> list) { 12 // 先排除权重为0的项 13 Iterator<Pair<K, V>> it = list.iterator(); 14 while (it.hasNext()) { 15 if (it.next().second.doubleValue() == 0) { 16 it.remove(); 17 } 18 } 19 20 for (Pair<K, V> pair : list) { 21 double lastWeight = this.weightMap.size() == 0 ? 0 : this.weightMap.lastKey();// 统一转为double 22 this.weightMap.put(pair.second.doubleValue() + lastWeight, pair.first);// 权重累加 23 } 24 } 25 26 public K random() { 27 double randomWeight = this.weightMap.lastKey() * Math.random(); 28 SortedMap<Double, K> tailMap = this.weightMap.tailMap(randomWeight, false); 29 return this.weightMap.get(tailMap.firstKey()); 30 } 31 32 }
算法验证:
1 /** 2 * Created by Jungle on 2020/3/31. 3 * 4 * @author JungleZhang 5 * @version 1.0.0 6 * @Description 7 */ 8 public class WeightRandomTest { 9 10 @Test 11 public void test() { 12 Pair<String, Integer> pair1 = new Pair<>("1", 1); 13 Pair<String, Integer> pair2 = new Pair<>("2", 2); 14 Pair<String, Integer> pair3 = new Pair<>("3", 3); 15 Pair<String, Integer> pair4 = new Pair<>("4", 4); 16 List<Pair<String, Integer>> list = new ArrayList<>(); 17 list.add(pair1); 18 list.add(pair2); 19 list.add(pair3); 20 list.add(pair4); 21 WeightRandom<String, Integer> random = new WeightRandom<>(list); 22 23 String num; 24 HashMap<String, Integer> totalCount = new HashMap<>(); 25 for (int i = 0; i < 10000000; i++) { 26 num = random.random(); 27 if (totalCount.containsKey(num)) { 28 totalCount.put(num, totalCount.get(num) + 1); 29 } else { 30 totalCount.put(num, 1); 31 } 32 } 33 System.out.println(totalCount.toString()); 34 35 } 36 }
运行1000w次,结果
{1=1000402, 2=1998608, 3=3000011, 4=4000979}
{1=1001041, 2=1999736, 3=3000950, 4=3998273}
{1=1000074, 2=1999378, 3=3000471, 4=4000077}
{1=1001806, 2=2001035, 3=3000200, 4=3996959}
{1=1000215, 2=2001900, 3=2995226, 4=4002659}
从结果上看,基本上满足了根据权重随机出的数据正确
这里使用TreeMap的tailMap()方法的特性,可以选出比该key值大的所有键值对
通过
1 for (Pair<K, V> pair : list) { 2 double lastWeight = this.weightMap.size() == 0 ? 0 : this.weightMap.lastKey();// 统一转为double 3 this.weightMap.put(pair.getValue().doubleValue() + lastWeight, pair.getKey());// 权重累加 4 }
这个方法,把值分段,比如例子中会分成 (1:1)(3:2)(6:3)(10:4)这四个键值对
使用
1 weightMap.lastKey() * Math.random()
可以得到一个权重累加值得随机数,最后根据tailMap() 得到第一个比权重随机数大的key值,这个key值就是我们随机到的值
Create by JungleZhang on 2020年3月31日17:29:28