我需要一个快速的算法从一个通用列表中选择5个随机元素。例如,我想从一个List<string>
中获取5个随机元素。
我需要一个快速的算法从一个通用列表中选择5个随机元素。例如,我想从一个List<string>
中获取5个随机元素。
你应该能够以O(subset.Length)的时间复杂度而不是O(originalList.Length)来完成这个操作。
基本上,您应该能够生成 subset
随机索引,然后从原始列表中获取它们。
public static class EnumerableExtensions {
public static Random randomizer = new Random(); // you'd ideally be able to replace this with whatever makes you comfortable
public static IEnumerable<T> GetRandom<T>(this IEnumerable<T> list, int numItems) {
return (list as T[] ?? list.ToArray()).GetRandom(numItems);
// because ReSharper whined about duplicate enumeration...
/*
items.Add(list.ElementAt(randomizer.Next(list.Count()))) ) numItems--;
*/
}
// just because the parentheses were getting confusing
public static IEnumerable<T> GetRandom<T>(this T[] list, int numItems) {
var items = new HashSet<T>(); // don't want to add the same item twice; otherwise use a list
while (numItems > 0 )
// if we successfully added it, move on
if( items.Add(list[randomizer.Next(list.Length)]) ) numItems--;
return items;
}
// and because it's really fun; note -- you may get repetition
public static IEnumerable<T> PluckRandomly<T>(this IEnumerable<T> list) {
while( true )
yield return list.ElementAt(randomizer.Next(list.Count()));
}
}
HashSet
而不是实际列表元素(如果您具有复杂类型或昂贵的比较);
[TestClass]
public class RandomizingTests : UnitTestBase {
[TestMethod]
public void GetRandomFromList() {
this.testGetRandomFromList((list, num) => list.GetRandom(num));
}
[TestMethod]
public void PluckRandomly() {
this.testGetRandomFromList((list, num) => list.PluckRandomly().Take(num), requireDistinct:false);
}
private void testGetRandomFromList(Func<IEnumerable<int>, int, IEnumerable<int>> methodToGetRandomItems, int numToTake = 10, int repetitions = 100000, bool requireDistinct = true) {
var items = Enumerable.Range(0, 100);
IEnumerable<int> randomItems = null;
while( repetitions-- > 0 ) {
randomItems = methodToGetRandomItems(items, numToTake);
Assert.AreEqual(numToTake, randomItems.Count(),
"Did not get expected number of items {0}; failed at {1} repetition--", numToTake, repetitions);
if(requireDistinct) Assert.AreEqual(numToTake, randomItems.Distinct().Count(),
"Collisions (non-unique values) found, failed at {0} repetition--", repetitions);
Assert.IsTrue(randomItems.All(o => items.Contains(o)),
"Some unknown values found; failed at {0} repetition--", repetitions);
}
}
}
这里有一种基于费舍尔-耶茨洗牌算法的实现,其算法复杂度为O(n),其中n是子集或样本大小,而不是列表大小,正如John Shedletsky所指出的那样。
public static IEnumerable<T> GetRandomSample<T>(this IList<T> list, int sampleSize)
{
if (list == null) throw new ArgumentNullException("list");
if (sampleSize > list.Count) throw new ArgumentException("sampleSize may not be greater than list count", "sampleSize");
var indices = new Dictionary<int, int>(); int index;
var rnd = new Random();
for (int i = 0; i < sampleSize; i++)
{
int j = rnd.Next(i, list.Count);
if (!indices.TryGetValue(j, out index)) index = j;
yield return list[index];
if (!indices.TryGetValue(i, out index)) index = i;
indices[j] = index;
}
}
public static List<T> GetTrueRandom<T>(this IList<T> source, int count,
bool throwArgumentOutOfRangeException = true)
{
if (throwArgumentOutOfRangeException && count > source.Count)
throw new ArgumentOutOfRangeException();
var randoms = new List<T>(count);
randoms.AddRandomly(source, count);
return randoms;
}
public static List<T> GetDistinctRandom<T>(this IList<T> source, int count)
{
if (count > source.Count)
throw new ArgumentOutOfRangeException();
if (count == source.Count)
return new List<T>(source);
var sourceDict = source.ToIndexedDictionary();
if (count > source.Count / 2)
{
while (sourceDict.Count > count)
sourceDict.Remove(source.GetRandomIndex());
return sourceDict.Select(kvp => kvp.Value).ToList();
}
var randomDict = new Dictionary<int, T>(count);
while (randomDict.Count < count)
{
int key = source.GetRandomIndex();
if (!randomDict.ContainsKey(key))
randomDict.Add(key, sourceDict[key]);
}
return randomDict.Select(kvp => kvp.Value).ToList();
}
3) 如果你需要真正独特的随机值,考虑原始组中的重复项,那么你可以使用与上面相同的方法,但是 HashSet
比字典更轻巧。
public static List<T> GetTrueDistinctRandom<T>(this IList<T> source, int count,
bool throwArgumentOutOfRangeException = true)
{
if (count > source.Count)
throw new ArgumentOutOfRangeException();
var set = new HashSet<T>(source);
if (throwArgumentOutOfRangeException && count > set.Count)
throw new ArgumentOutOfRangeException();
List<T> list = hash.ToList();
if (count >= set.Count)
return list;
if (count > set.Count / 2)
{
while (set.Count > count)
set.Remove(list.GetRandom());
return set.ToList();
}
var randoms = new HashSet<T>();
randoms.AddRandomly(list, count);
return randoms.ToList();
}
randoms
变量被设置为HashSet
,以避免在输入列表很小的情况下Random.Next
产生相同值的稀有情况下重复添加。
所以{1, 2, 2, 4} => 3个随机项 => {1, 2, 4} 而不是 {1, 2, 2}
{1, 2, 2, 4} => 4个随机项 => 异常!!或{1, 2, 4}取决于设置的标志。
我使用了一些扩展方法:
static Random rnd = new Random();
public static int GetRandomIndex<T>(this ICollection<T> source)
{
return rnd.Next(source.Count);
}
public static T GetRandom<T>(this IList<T> source)
{
return source[source.GetRandomIndex()];
}
static void AddRandomly<T>(this ICollection<T> toCol, IList<T> fromList, int count)
{
while (toCol.Count < count)
toCol.Add(fromList.GetRandom());
}
public static Dictionary<int, T> ToIndexedDictionary<T>(this IEnumerable<T> lst)
{
return lst.ToIndexedDictionary(t => t);
}
public static Dictionary<int, T> ToIndexedDictionary<S, T>(this IEnumerable<S> lst,
Func<S, T> valueSelector)
{
int index = -1;
return lst.ToDictionary(t => ++index, valueSelector);
}
System.Random
更快的随机类, 但我认为这并不重要,因为后者很可能永远不会成为瓶颈,它已经足够快了。
编辑:如果您还需要重新排列返回项目的顺序,则没有什么能击败dhakim的Fisher-Yates方法 - 简单明了。// Instead of this
YourList.OrderBy(x => rnd.Next()).Take(5)
// Temporarily transform
YourList
.Select(v => new {v, i = rnd.Next()}) // Associate a random index to each entry
.OrderBy(x => x.i).Take(5) // Sort by (at this point fixed) random index
.Select(x => x.v); // Go back to enumerable of entry
List<Object> temp = OriginalList.ToList();
List<Object> selectedItems = new List<Object>();
Random rnd = new Random();
Object o;
int i = 0;
while (i < NumberOfSelectedItems)
{
o = temp[rnd.Next(temp.Count)];
selectedItems.Add(o);
temp.Remove(o);
i++;
}
这是我在第一次尝试中能够想到的最好翻译:
public List<String> getRandomItemsFromList(int returnCount, List<String> list)
{
List<String> returnList = new List<String>();
Dictionary<int, int> randoms = new Dictionary<int, int>();
while (randoms.Count != returnCount)
{
//generate new random between one and total list count
int randomInt = new Random().Next(list.Count);
// store this in dictionary to ensure uniqueness
try
{
randoms.Add(randomInt, randomInt);
}
catch (ArgumentException aex)
{
Console.Write(aex.Message);
} //we can assume this element exists in the dictonary already
//check for randoms length and then iterate through the original list
//adding items we select via random to the return list
if (randoms.Count == returnCount)
{
foreach (int key in randoms.Keys)
returnList.Add(list[randoms[key]]);
break; //break out of _while_ loop
}
}
return returnList;
}
使用在1-总列表计数范围内的随机列表,然后简单地提取该列表中的那些项目似乎是最好的方法,但是我仍在考虑使用字典来确保唯一性。
还要注意我使用了一个字符串列表,根据需要进行替换。
这种方法可能与Kyle的方法相等。
假设您的列表大小为n,您想要k个元素。
Random rand = new Random();
for(int i = 0; k>0; ++i)
{
int r = rand.Next(0, n-i);
if(r<k)
{
//include element i
k--;
}
}
效果非常好 :)
-Alex Gilbert
Random.Next()
非常慢,如果您为每个选定的项目生成一个随机数,则会获得性能优势。static void Main()
{
BenchmarkRunner.Run<Benchmarks>();
new Benchmarks() { ListSize = 100, SelectionSize = 10 }
.BenchmarkStdDev();
}
[MemoryDiagnoser]
public class Benchmarks
{
[Params(50, 500, 5000)]
public int ListSize;
[Params(5, 10, 25, 50)]
public int SelectionSize;
private Random _rnd;
private List<int> _list;
private int[] _hits;
[GlobalSetup]
public void Setup()
{
_rnd = new Random(12345);
_list = Enumerable.Range(0, ListSize).ToList();
_hits = new int[ListSize];
}
[Benchmark]
public void Test_IterateSelect()
=> Random_IterateSelect(_list, SelectionSize).ToList();
[Benchmark]
public void Test_RandomIndices()
=> Random_RandomIdices(_list, SelectionSize).ToList();
[Benchmark]
public void Test_FisherYates()
=> Random_FisherYates(_list, SelectionSize).ToList();
public void BenchmarkStdDev()
{
RunOnce(Random_IterateSelect, "IterateSelect");
RunOnce(Random_RandomIdices, "RandomIndices");
RunOnce(Random_FisherYates, "FisherYates");
void RunOnce(Func<IEnumerable<int>, int, IEnumerable<int>> method, string methodName)
{
Setup();
for (int i = 0; i < 1000000; i++)
{
var selected = method(_list, SelectionSize).ToList();
Debug.Assert(selected.Count() == SelectionSize);
foreach (var item in selected) _hits[item]++;
}
var stdDev = GetStdDev(_hits);
Console.WriteLine($"StdDev of {methodName}: {stdDev :n} (% of average: {stdDev / (_hits.Average() / 100) :n})");
}
double GetStdDev(IEnumerable<int> hits)
{
var average = hits.Average();
return Math.Sqrt(hits.Average(v => Math.Pow(v - average, 2)));
}
}
public IEnumerable<T> Random_IterateSelect<T>(IEnumerable<T> collection, int needed)
{
var count = collection.Count();
for (int i = 0; i < count; i++)
{
if (_rnd.Next(count - i) < needed)
{
yield return collection.ElementAt(i);
if (--needed == 0)
yield break;
}
}
}
public IEnumerable<T> Random_RandomIdices<T>(IEnumerable<T> list, int needed)
{
var selectedItems = new HashSet<T>();
var count = list.Count();
while (needed > 0)
if (selectedItems.Add(list.ElementAt(_rnd.Next(count))))
needed--;
return selectedItems;
}
public IEnumerable<T> Random_FisherYates<T>(IEnumerable<T> list, int sampleSize)
{
var count = list.Count();
if (sampleSize > count) throw new ArgumentException("sampleSize may not be greater than list count", "sampleSize");
var indices = new Dictionary<int, int>(); int index;
for (int i = 0; i < sampleSize; i++)
{
int j = _rnd.Next(i, count);
if (!indices.TryGetValue(j, out index)) index = j;
yield return list.ElementAt(index);
if (!indices.TryGetValue(i, out index)) index = i;
indices[j] = index;
}
}
}
输出:
| Method | ListSize | Select | Mean | Error | StdDev | Gen 0 | Allocated |
|-------------- |--------- |------- |------------:|----------:|----------:|-------:|----------:|
| IterateSelect | 50 | 5 | 711.5 ns | 5.19 ns | 4.85 ns | 0.0305 | 144 B |
| RandomIndices | 50 | 5 | 341.1 ns | 4.48 ns | 4.19 ns | 0.0644 | 304 B |
| FisherYates | 50 | 5 | 573.5 ns | 6.12 ns | 5.72 ns | 0.0944 | 447 B |
| IterateSelect | 50 | 10 | 967.2 ns | 4.64 ns | 3.87 ns | 0.0458 | 220 B |
| RandomIndices | 50 | 10 | 709.9 ns | 11.27 ns | 9.99 ns | 0.1307 | 621 B |
| FisherYates | 50 | 10 | 1,204.4 ns | 10.63 ns | 9.94 ns | 0.1850 | 875 B |
| IterateSelect | 50 | 25 | 1,358.5 ns | 7.97 ns | 6.65 ns | 0.0763 | 361 B |
| RandomIndices | 50 | 25 | 1,958.1 ns | 15.69 ns | 13.91 ns | 0.2747 | 1298 B |
| FisherYates | 50 | 25 | 2,878.9 ns | 31.42 ns | 29.39 ns | 0.3471 | 1653 B |
| IterateSelect | 50 | 50 | 1,739.1 ns | 15.86 ns | 14.06 ns | 0.1316 | 629 B |
| RandomIndices | 50 | 50 | 8,906.1 ns | 88.92 ns | 74.25 ns | 0.5951 | 2848 B |
| FisherYates | 50 | 50 | 4,899.9 ns | 38.10 ns | 33.78 ns | 0.4349 | 2063 B |
| IterateSelect | 500 | 5 | 4,775.3 ns | 46.96 ns | 41.63 ns | 0.0305 | 144 B |
| RandomIndices | 500 | 5 | 327.8 ns | 2.82 ns | 2.50 ns | 0.0644 | 304 B |
| FisherYates | 500 | 5 | 558.5 ns | 7.95 ns | 7.44 ns | 0.0944 | 449 B |
| IterateSelect | 500 | 10 | 5,387.1 ns | 44.57 ns | 41.69 ns | 0.0458 | 220 B |
| RandomIndices | 500 | 10 | 648.0 ns | 9.12 ns | 8.54 ns | 0.1307 | 621 B |
| FisherYates | 500 | 10 | 1,154.6 ns | 13.66 ns | 12.78 ns | 0.1869 | 889 B |
| IterateSelect | 500 | 25 | 6,442.3 ns | 48.90 ns | 40.83 ns | 0.0763 | 361 B |
| RandomIndices | 500 | 25 | 1,569.6 ns | 15.79 ns | 14.77 ns | 0.2747 | 1298 B |
| FisherYates | 500 | 25 | 2,726.1 ns | 25.32 ns | 22.44 ns | 0.3777 | 1795 B |
| IterateSelect | 500 | 50 | 7,775.4 ns | 35.47 ns | 31.45 ns | 0.1221 | 629 B |
| RandomIndices | 500 | 50 | 2,976.9 ns | 27.11 ns | 24.03 ns | 0.6027 | 2848 B |
| FisherYates | 500 | 50 | 5,383.2 ns | 36.49 ns | 32.35 ns | 0.8163 | 3870 B |
| IterateSelect | 5000 | 5 | 45,208.6 ns | 459.92 ns | 430.21 ns | - | 144 B |
| RandomIndices | 5000 | 5 | 328.7 ns | 5.15 ns | 4.81 ns | 0.0644 | 304 B |
| FisherYates | 5000 | 5 | 556.1 ns | 10.75 ns | 10.05 ns | 0.0944 | 449 B |
| IterateSelect | 5000 | 10 | 49,253.9 ns | 420.26 ns | 393.11 ns | - | 220 B |
| RandomIndices | 5000 | 10 | 642.9 ns | 4.95 ns | 4.13 ns | 0.1307 | 621 B |
| FisherYates | 5000 | 10 | 1,141.9 ns | 12.81 ns | 11.98 ns | 0.1869 | 889 B |
| IterateSelect | 5000 | 25 | 54,044.4 ns | 208.92 ns | 174.46 ns | 0.0610 | 361 B |
| RandomIndices | 5000 | 25 | 1,480.5 ns | 11.56 ns | 10.81 ns | 0.2747 | 1298 B |
| FisherYates | 5000 | 25 | 2,713.9 ns | 27.31 ns | 24.21 ns | 0.3777 | 1795 B |
| IterateSelect | 5000 | 50 | 54,418.2 ns | 329.62 ns | 308.32 ns | 0.1221 | 629 B |
| RandomIndices | 5000 | 50 | 2,886.4 ns | 36.53 ns | 34.17 ns | 0.6027 | 2848 B |
| FisherYates | 5000 | 50 | 5,347.2 ns | 59.45 ns | 55.61 ns | 0.8163 | 3870 B |
StdDev of IterateSelect: 671.88 (% of average: 0.67)
StdDev of RandomIndices: 296.07 (% of average: 0.30)
StdDev of FisherYates: 280.47 (% of average: 0.28)
根据Kyle的答案,这是我的c#实现。
/// <summary>
/// Picks random selection of available game ID's
/// </summary>
private static List<int> GetRandomGameIDs(int count)
{
var gameIDs = (int[])HttpContext.Current.Application["NonDeletedArcadeGameIDs"];
var totalGameIDs = gameIDs.Count();
if (count > totalGameIDs) count = totalGameIDs;
var rnd = new Random();
var leftToPick = count;
var itemsLeft = totalGameIDs;
var arrPickIndex = 0;
var returnIDs = new List<int>();
while (leftToPick > 0)
{
if (rnd.Next(0, itemsLeft) < leftToPick)
{
returnIDs .Add(gameIDs[arrPickIndex]);
leftToPick--;
}
arrPickIndex++;
itemsLeft--;
}
return returnIDs ;
}
目标:从集合源中选择N个项目,不重复。 我为任何通用集合创建了一个扩展。以下是我的方法:
public static class CollectionExtension
{
public static IList<TSource> RandomizeCollection<TSource>(this IList<TSource> source, int maxItems)
{
int randomCount = source.Count > maxItems ? maxItems : source.Count;
int?[] randomizedIndices = new int?[randomCount];
Random random = new Random();
for (int i = 0; i < randomizedIndices.Length; i++)
{
int randomResult = -1;
while (randomizedIndices.Contains((randomResult = random.Next(0, source.Count))))
{
//0 -> since all list starts from index 0; source.Count -> maximum number of items that can be randomize
//continue looping while the generated random number is already in the list of randomizedIndices
}
randomizedIndices[i] = randomResult;
}
IList<TSource> result = new List<TSource>();
foreach (int index in randomizedIndices)
result.Add(source.ElementAt(index));
return result;
}
}
C#
中的List<string>
,用户特别想要一个快速简单的解决方案。(显然,答案是排序并取五个。如果您在领域内做任何其他事情,那将是极其糟糕的工程。请注意,当然可以编造... - Fattie