我正在寻找优先级队列或堆数据结构的.NET实现

Priority queues are data structures that provide more flexibility than simple sorting, because they allow new elements to enter a system at arbitrary intervals. It is much more cost-effective to insert a new job into a priority queue than to re-sort everything on each such arrival. The basic priority queue supports three primary operations: Insert(Q,x). Given an item x with key k, insert it into the priority queue Q. Find-Minimum(Q). Return a pointer to the item whose key value is smaller than any other key in the priority queue Q. Delete-Minimum(Q). Remove the item from the priority queue Q whose key is minimum

除非我找错地方了,否则框架里没有。有谁知道好的吗,还是我自己卷?


我喜欢使用PowerCollections中的OrderedBag和OrderedSet类作为优先队列。


在Java Collections框架中的Java实现(Java .util. priorityqueue)上使用Java到c#的转换器,或者更智能地使用算法和核心代码,并将其插入到您自己制作的c#类中,该类遵循c# Collections框架用于队列的API,或者至少是Collections。


我在Julian Bucknall的博客(http://www.boyet.com/Articles/PriorityQueueCSharp3.html)上找到了一个

我们稍微修改了一下,以便队列中优先级低的项目最终会随着时间的推移“上升”到顶部,这样它们就不会挨饿了。


你可能会喜欢C5泛型集合库中的IntervalHeap。引用用户指南

类IntervalHeap<T>使用存储为对数组的间隔堆实现接口IPriorityQueue<T>。FindMin和 FindMax操作和索引器的get-访问器花费的时间为O(1)。DeleteMin, DeleteMax、Add和Update操作,以及索引器的集访问器,都需要时间 O(log n)。与普通优先级队列相比,间隔堆提供了两个最小优先级队列 同样效率的最大操作。

API非常简单

> var heap = new C5.IntervalHeap<int>();
> heap.Add(10);
> heap.Add(5);
> heap.FindMin();
5

从Nuget https://www.nuget.org/packages/C5或GitHub https://github.com/sestoft/C5/安装


你可能会发现这个实现很有用: http://www.codeproject.com/Articles/126751/Priority-queue-in-Csharp-with-help-of-heap-data-st.aspx

它是通用的,基于堆数据结构


这是我刚刚写的一个,也许它没有那么优化(只是使用了一个排序的字典),但很容易理解。 您可以插入不同类型的对象,因此没有泛型队列。

using System;
using System.Diagnostics;
using System.Collections;
using System.Collections.Generic;

namespace PrioQueue
{
    public class PrioQueue
    {
        int total_size;
        SortedDictionary<int, Queue> storage;

        public PrioQueue ()
        {
            this.storage = new SortedDictionary<int, Queue> ();
            this.total_size = 0;
        }

        public bool IsEmpty ()
        {
            return (total_size == 0);
        }

        public object Dequeue ()
        {
            if (IsEmpty ()) {
                throw new Exception ("Please check that priorityQueue is not empty before dequeing");
            } else
                foreach (Queue q in storage.Values) {
                    // we use a sorted dictionary
                    if (q.Count > 0) {
                        total_size--;
                        return q.Dequeue ();
                    }
                }

                Debug.Assert(false,"not supposed to reach here. problem with changing total_size");

                return null; // not supposed to reach here.
        }

        // same as above, except for peek.

        public object Peek ()
        {
            if (IsEmpty ())
                throw new Exception ("Please check that priorityQueue is not empty before peeking");
            else
                foreach (Queue q in storage.Values) {
                    if (q.Count > 0)
                        return q.Peek ();
                }

                Debug.Assert(false,"not supposed to reach here. problem with changing total_size");

                return null; // not supposed to reach here.
        }

        public object Dequeue (int prio)
        {
            total_size--;
            return storage[prio].Dequeue ();
        }

        public void Enqueue (object item, int prio)
        {
            if (!storage.ContainsKey (prio)) {
                storage.Add (prio, new Queue ());
              }
            storage[prio].Enqueue (item);
            total_size++;

        }
    }
}

下面是来自NGenerics团队的另一个实现:

NGenerics PriorityQueue


下面是我对.NET堆的尝试

public abstract class Heap<T> : IEnumerable<T>
{
    private const int InitialCapacity = 0;
    private const int GrowFactor = 2;
    private const int MinGrow = 1;

    private int _capacity = InitialCapacity;
    private T[] _heap = new T[InitialCapacity];
    private int _tail = 0;

    public int Count { get { return _tail; } }
    public int Capacity { get { return _capacity; } }

    protected Comparer<T> Comparer { get; private set; }
    protected abstract bool Dominates(T x, T y);

    protected Heap() : this(Comparer<T>.Default)
    {
    }

    protected Heap(Comparer<T> comparer) : this(Enumerable.Empty<T>(), comparer)
    {
    }

    protected Heap(IEnumerable<T> collection)
        : this(collection, Comparer<T>.Default)
    {
    }

    protected Heap(IEnumerable<T> collection, Comparer<T> comparer)
    {
        if (collection == null) throw new ArgumentNullException("collection");
        if (comparer == null) throw new ArgumentNullException("comparer");

        Comparer = comparer;

        foreach (var item in collection)
        {
            if (Count == Capacity)
                Grow();

            _heap[_tail++] = item;
        }

        for (int i = Parent(_tail - 1); i >= 0; i--)
            BubbleDown(i);
    }

    public void Add(T item)
    {
        if (Count == Capacity)
            Grow();

        _heap[_tail++] = item;
        BubbleUp(_tail - 1);
    }

    private void BubbleUp(int i)
    {
        if (i == 0 || Dominates(_heap[Parent(i)], _heap[i])) 
            return; //correct domination (or root)

        Swap(i, Parent(i));
        BubbleUp(Parent(i));
    }

    public T GetMin()
    {
        if (Count == 0) throw new InvalidOperationException("Heap is empty");
        return _heap[0];
    }

    public T ExtractDominating()
    {
        if (Count == 0) throw new InvalidOperationException("Heap is empty");
        T ret = _heap[0];
        _tail--;
        Swap(_tail, 0);
        BubbleDown(0);
        return ret;
    }

    private void BubbleDown(int i)
    {
        int dominatingNode = Dominating(i);
        if (dominatingNode == i) return;
        Swap(i, dominatingNode);
        BubbleDown(dominatingNode);
    }

    private int Dominating(int i)
    {
        int dominatingNode = i;
        dominatingNode = GetDominating(YoungChild(i), dominatingNode);
        dominatingNode = GetDominating(OldChild(i), dominatingNode);

        return dominatingNode;
    }

    private int GetDominating(int newNode, int dominatingNode)
    {
        if (newNode < _tail && !Dominates(_heap[dominatingNode], _heap[newNode]))
            return newNode;
        else
            return dominatingNode;
    }

    private void Swap(int i, int j)
    {
        T tmp = _heap[i];
        _heap[i] = _heap[j];
        _heap[j] = tmp;
    }

    private static int Parent(int i)
    {
        return (i + 1)/2 - 1;
    }

    private static int YoungChild(int i)
    {
        return (i + 1)*2 - 1;
    }

    private static int OldChild(int i)
    {
        return YoungChild(i) + 1;
    }

    private void Grow()
    {
        int newCapacity = _capacity*GrowFactor + MinGrow;
        var newHeap = new T[newCapacity];
        Array.Copy(_heap, newHeap, _capacity);
        _heap = newHeap;
        _capacity = newCapacity;
    }

    public IEnumerator<T> GetEnumerator()
    {
        return _heap.Take(Count).GetEnumerator();
    }

    IEnumerator IEnumerable.GetEnumerator()
    {
        return GetEnumerator();
    }
}

public class MaxHeap<T> : Heap<T>
{
    public MaxHeap()
        : this(Comparer<T>.Default)
    {
    }

    public MaxHeap(Comparer<T> comparer)
        : base(comparer)
    {
    }

    public MaxHeap(IEnumerable<T> collection, Comparer<T> comparer)
        : base(collection, comparer)
    {
    }

    public MaxHeap(IEnumerable<T> collection) : base(collection)
    {
    }

    protected override bool Dominates(T x, T y)
    {
        return Comparer.Compare(x, y) >= 0;
    }
}

public class MinHeap<T> : Heap<T>
{
    public MinHeap()
        : this(Comparer<T>.Default)
    {
    }

    public MinHeap(Comparer<T> comparer)
        : base(comparer)
    {
    }

    public MinHeap(IEnumerable<T> collection) : base(collection)
    {
    }

    public MinHeap(IEnumerable<T> collection, Comparer<T> comparer)
        : base(collection, comparer)
    {
    }

    protected override bool Dominates(T x, T y)
    {
        return Comparer.Compare(x, y) <= 0;
    }
}

一些测试:

[TestClass]
public class HeapTests
{
    [TestMethod]
    public void TestHeapBySorting()
    {
        var minHeap = new MinHeap<int>(new[] {9, 8, 4, 1, 6, 2, 7, 4, 1, 2});
        AssertHeapSort(minHeap, minHeap.OrderBy(i => i).ToArray());

        minHeap = new MinHeap<int> { 7, 5, 1, 6, 3, 2, 4, 1, 2, 1, 3, 4, 7 };
        AssertHeapSort(minHeap, minHeap.OrderBy(i => i).ToArray());

        var maxHeap = new MaxHeap<int>(new[] {1, 5, 3, 2, 7, 56, 3, 1, 23, 5, 2, 1});
        AssertHeapSort(maxHeap, maxHeap.OrderBy(d => -d).ToArray());

        maxHeap = new MaxHeap<int> {2, 6, 1, 3, 56, 1, 4, 7, 8, 23, 4, 5, 7, 34, 1, 4};
        AssertHeapSort(maxHeap, maxHeap.OrderBy(d => -d).ToArray());
    }

    private static void AssertHeapSort(Heap<int> heap, IEnumerable<int> expected)
    {
        var sorted = new List<int>();
        while (heap.Count > 0)
            sorted.Add(heap.ExtractDominating());

        Assert.IsTrue(sorted.SequenceEqual(expected));
    }
}

下面的PriorityQueue实现使用了System库中的SortedSet。

using System;
using System.Collections.Generic;

namespace CDiggins
{
    interface IPriorityQueue<T, K> where K : IComparable<K>
    {
        bool Empty { get; }
        void Enqueue(T x, K key);
        void Dequeue();
        T Top { get; }
    }

    class PriorityQueue<T, K> : IPriorityQueue<T, K> where K : IComparable<K>
    {
        SortedSet<Tuple<T, K>> set;

        class Comparer : IComparer<Tuple<T, K>> {
            public int Compare(Tuple<T, K> x, Tuple<T, K> y) {
                return x.Item2.CompareTo(y.Item2);
            }
        }

        PriorityQueue() { set = new SortedSet<Tuple<T, K>>(new Comparer()); }
        public bool Empty { get { return set.Count == 0;  } }
        public void Enqueue(T x, K key) { set.Add(Tuple.Create(x, key)); }
        public void Dequeue() { set.Remove(set.Max); }
        public T Top { get { return set.Max.Item1; } }
    }
}

class PriorityQueue<T>
{
    IComparer<T> comparer;
    T[] heap;
    public int Count { get; private set; }
    public PriorityQueue() : this(null) { }
    public PriorityQueue(int capacity) : this(capacity, null) { }
    public PriorityQueue(IComparer<T> comparer) : this(16, comparer) { }
    public PriorityQueue(int capacity, IComparer<T> comparer)
    {
        this.comparer = (comparer == null) ? Comparer<T>.Default : comparer;
        this.heap = new T[capacity];
    }
    public void push(T v)
    {
        if (Count >= heap.Length) Array.Resize(ref heap, Count * 2);
        heap[Count] = v;
        SiftUp(Count++);
    }
    public T pop()
    {
        var v = top();
        heap[0] = heap[--Count];
        if (Count > 0) SiftDown(0);
        return v;
    }
    public T top()
    {
        if (Count > 0) return heap[0];
        throw new InvalidOperationException("优先队列为空");
    }
    void SiftUp(int n)
    {
        var v = heap[n];
        for (var n2 = n / 2; n > 0 && comparer.Compare(v, heap[n2]) > 0; n = n2, n2 /= 2) heap[n] = heap[n2];
        heap[n] = v;
    }
    void SiftDown(int n)
    {
        var v = heap[n];
        for (var n2 = n * 2; n2 < Count; n = n2, n2 *= 2)
        {
            if (n2 + 1 < Count && comparer.Compare(heap[n2 + 1], heap[n2]) > 0) n2++;
            if (comparer.Compare(v, heap[n2]) >= 0) break;
            heap[n] = heap[n2];
        }
        heap[n] = v;
    }
}

一件容易的事。


我最近也遇到了同样的问题,最后为此创建了一个NuGet包。

这实现了一个标准的基于堆的优先级队列。它还具有BCL集合的所有常见的优点:ICollection<T>和IReadOnlyCollection<T>实现,自定义IComparer<T>支持,指定初始容量的能力,以及使集合更容易在调试器中使用的DebuggerTypeProxy。

还有一个内联版本的包,它只安装一个.cs文件到你的项目中(如果你想避免外部可见的依赖关系,这很有用)。

更多信息请访问github页面。


AlgoKit

我写了一个名为AlgoKit的开源库,可以通过NuGet获得。它包含:

隐式d-ary堆(ArrayHeap), 二项堆, 配对堆。

代码已经经过了广泛的测试。我强烈建议你试一试。

例子

var comparer = Comparer<int>.Default;
var heap = new PairingHeap<int, string>(comparer);

heap.Add(3, "your");
heap.Add(5, "of");
heap.Add(7, "disturbing.");
heap.Add(2, "find");
heap.Add(1, "I");
heap.Add(6, "faith");
heap.Add(4, "lack");

while (!heap.IsEmpty)
    Console.WriteLine(heap.Pop().Value);

为什么有三堆?

实现的最佳选择强烈依赖于输入-正如Larkin, Sen和Tarjan在优先队列的回归基础经验研究中所显示的,arXiv:1403.0252v1 [cs.DS]。他们测试了隐式d-ary堆、配对堆、斐波那契堆、二项式堆、显式d-ary堆、排名配对堆、震动堆、违反堆、排名放松的弱堆和严格斐波那契堆。

AlgoKit具有三种类型的堆,在测试中似乎是最有效的。

关于选择的提示

对于数量相对较少的元素,您可能会对使用隐式堆感兴趣,特别是第四元堆(隐式4元)。在操作较大堆大小的情况下,像二项式堆和配对堆这样的平摊结构应该执行得更好。


一个简单的最大堆实现。

https://github.com/bharathkumarms/AlgorithmsMadeEasy/blob/master/AlgorithmsMadeEasy/MaxHeap.cs

using System;
using System.Collections.Generic;
using System.Linq;

namespace AlgorithmsMadeEasy
{
    class MaxHeap
    {
        private static int capacity = 10;
        private int size = 0;
        int[] items = new int[capacity];

        private int getLeftChildIndex(int parentIndex) { return 2 * parentIndex + 1; }
        private int getRightChildIndex(int parentIndex) { return 2 * parentIndex + 2; }
        private int getParentIndex(int childIndex) { return (childIndex - 1) / 2; }

        private int getLeftChild(int parentIndex) { return this.items[getLeftChildIndex(parentIndex)]; }
        private int getRightChild(int parentIndex) { return this.items[getRightChildIndex(parentIndex)]; }
        private int getParent(int childIndex) { return this.items[getParentIndex(childIndex)]; }

        private bool hasLeftChild(int parentIndex) { return getLeftChildIndex(parentIndex) < size; }
        private bool hasRightChild(int parentIndex) { return getRightChildIndex(parentIndex) < size; }
        private bool hasParent(int childIndex) { return getLeftChildIndex(childIndex) > 0; }

        private void swap(int indexOne, int indexTwo)
        {
            int temp = this.items[indexOne];
            this.items[indexOne] = this.items[indexTwo];
            this.items[indexTwo] = temp;
        }

        private void hasEnoughCapacity()
        {
            if (this.size == capacity)
            {
                Array.Resize(ref this.items,capacity*2);
                capacity *= 2;
            }
        }

        public void Add(int item)
        {
            this.hasEnoughCapacity();
            this.items[size] = item;
            this.size++;
            heapifyUp();
        }

        public int Remove()
        {
            int item = this.items[0];
            this.items[0] = this.items[size-1];
            this.items[this.size - 1] = 0;
            size--;
            heapifyDown();
            return item;
        }

        private void heapifyUp()
        {
            int index = this.size - 1;
            while (hasParent(index) && this.items[index] > getParent(index))
            {
                swap(index, getParentIndex(index));
                index = getParentIndex(index);
            }
        }

        private void heapifyDown()
        {
            int index = 0;
            while (hasLeftChild(index))
            {
                int bigChildIndex = getLeftChildIndex(index);
                if (hasRightChild(index) && getLeftChild(index) < getRightChild(index))
                {
                    bigChildIndex = getRightChildIndex(index);
                }

                if (this.items[bigChildIndex] < this.items[index])
                {
                    break;
                }
                else
                {
                    swap(bigChildIndex,index);
                    index = bigChildIndex;
                }
            }
        }
    }
}

/*
Calling Code:
    MaxHeap mh = new MaxHeap();
    mh.Add(10);
    mh.Add(5);
    mh.Add(2);
    mh.Add(1);
    mh.Add(50);
    int maxVal  = mh.Remove();
    int newMaxVal = mh.Remove();
*/

. net 6+:正如@rustyx评论的那样,. net 6添加了System.Collections.Generic。PriorityQueue < TElement, TPriority >类。FWIW是开源的,用c#实现的。

早期的。net核心版本和。net框架:微软在。net框架中编写(并在线共享)了2个内部的PriorityQueue类。然而,正如@ mathumum -mut所评论的那样,其中有一个错误(SO社区当然已经提供了修复):微软内部PriorityQueue<T>?