I am looking for a .NET implementation of a priority queue or heap data structure
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
Unless I am looking in the wrong place, there isn't one in the framework. Is anyone aware of a good one, or should I roll my own?
This question is related to
c#
.net
data-structures
heap
priority-queue
I like using the OrderedBag
and OrderedSet
classes in PowerCollections as priority queues.
I wrote an open source library called AlgoKit, available via NuGet. It contains:
The code has been extensively tested. I definitely recommend you to give it a try.
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);
The optimal choice of implementation is strongly input-dependent — as Larkin, Sen, and Tarjan show in A back-to-basics empirical study of priority queues, arXiv:1403.0252v1 [cs.DS]. They tested implicit d-ary heaps, pairing heaps, Fibonacci heaps, binomial heaps, explicit d-ary heaps, rank-pairing heaps, quake heaps, violation heaps, rank-relaxed weak heaps, and strict Fibonacci heaps.
AlgoKit features three types of heaps that appeared to be most efficient among those tested.
For a relatively small number of elements, you would likely be interested in using implicit heaps, especially quaternary heaps (implicit 4-ary). In case of operating on larger heap sizes, amortized structures like binomial heaps and pairing heaps should perform better.
Here's my attempt at a .NET heap
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;
}
}
Some tests:
[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));
}
}
Use a Java to C# translator on the Java implementation (java.util.PriorityQueue) in the Java Collections framework, or more intelligently use the algorithm and core code and plug it into a C# class of your own making that adheres to the C# Collections framework API for Queues, or at least Collections.
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("??????");
}
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;
}
}
easy.
here's one i just wrote, maybe it's not as optimized (just uses a sorted dictionary) but simple to understand. you can insert objects of different kinds, so no generic queues.
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++;
}
}
}
A Simple Max Heap Implementation.
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();
*/
I had the same issue recently and ended up creating a NuGet package for this.
This implements a standard heap-based priority queue. It also has all the usual niceties of the BCL collections: ICollection<T>
and IReadOnlyCollection<T>
implementation, custom IComparer<T>
support, ability to specify an initial capacity, and a DebuggerTypeProxy
to make the collection easier to work with in the debugger.
There is also an Inline version of the package which just installs a single .cs file into your project (useful if you want to avoid taking externally-visible dependencies).
More information is available on the github page.
The following implementation of a PriorityQueue
uses SortedSet
from the System library.
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; } }
}
}
You might like IntervalHeap from the C5 Generic Collection Library. To quote the user guide
Class
IntervalHeap<T>
implements interfaceIPriorityQueue<T>
using an interval heap stored as an array of pairs. TheFindMin
andFindMax
operations, and the indexer’s get-accessor, take time O(1). TheDeleteMin
,DeleteMax
, Add and Update operations, and the indexer’s set-accessor, take time O(log n). In contrast to an ordinary priority queue, an interval heap offers both minimum and maximum operations with the same efficiency.
The API is simple enough
> var heap = new C5.IntervalHeap<int>();
> heap.Add(10);
> heap.Add(5);
> heap.FindMin();
5
Install from Nuget https://www.nuget.org/packages/C5 or GitHub https://github.com/sestoft/C5/
Here is the another implementation from NGenerics team:
I found one by Julian Bucknall on his blog here - http://www.boyet.com/Articles/PriorityQueueCSharp3.html
We modified it slightly so that low-priority items on the queue would eventually 'bubble-up' to the top over time, so they wouldn't suffer starvation.
You may find useful this implementation: http://www.codeproject.com/Articles/126751/Priority-queue-in-Csharp-with-help-of-heap-data-st.aspx
it is generic and based on heap data structure
As mentioned in Microsoft Collections for .NET, Microsoft has written (and shared online) 2 internal PriorityQueue classes within the .NET Framework. Their code is available to try out.
As @mathusum-mut commented, there is a bug in one of Microsoft's internal PriorityQueue
classes (the SO community has, of course, provided fixes for it): Bug in Microsoft's internal PriorityQueue<T>?
Source: Stackoverflow.com