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?
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();
*/
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; } }
}
}
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++;
}
}
}
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。
