I'm supposed to write a LIFO (last in first out) class for chars, which can be edited by Pop and Add functions and seen by Peek function or foreach. Class is working on array to be more optimalized, but foreach for some reason's not working. I tried to make function GetEnumerator based on return value of _arr.GetEnumerator() function, but it was not working, because when I printed item, in console was shown TestApp.LIFO, so I made this, but now foreach won't print a single item and by debuging _i value on Reset function is 0. Can someone say why is it happening and suggest solution?
using System;
using System.Collections;
using System.Collections.Generic;
namespace TestApp {
internal class LIFO : IEnumerator, IEnumerable {
public LIFO(int size) {
_arr = new char[size];
_index = 0;
}
public LIFO(char[] arr) {
_arr = arr.Clone() as char[];
_index = 0;
}
public char Peek() => _index == 0 ? '\0' : _arr[_index - 1];
public bool Add(char c) {
if (_index == _arr.Length)
return false;
try {
_arr[_index] = c;
} catch (Exception) {
return false;
}
++_index;
return true;
}
public void Pop() {
if (_index == 0)
return;
_arr[--_index] = '\0';
}
private int _i;
public IEnumerator GetEnumerator() => this;
public bool MoveNext() => --_i > -1;
public void Reset() => _i = _index - 1;
public object Current => _arr[_i];
private int _index;
private readonly char[] _arr;
}
}
In Program.cs:
using System;
namespace TestApp {
internal static class Program {
private static void Main() {
LIFO l = new(17);
l.Add('k');
l.Add('h');
l.Add('c');
foreach (var item in l)
Console.WriteLine(l);
}
}
}
The issue is that Reset is not called. It is no longer needed but the interface is not changed due to backwards compatibility. Since new iterators implemented using yield return is actually required to throw an exception if Reset is called, no code is expected to call this method anymore.
As such, your iterator index variable, _i, is never initialized and stays at 0. The first call to MoveNext steps it below 0 and then returns false, ending the foreach loop before it even started.
You should always decouple the iterators from your actual collection as it should be safe to enumerate the same collection twice in a nested manner, storing the index variable as an instance variable in your collection prevents this.
You can, however, simplify the enumerator implementation vastly by using yield return like this:
public class LIFO : IEnumerable
{
...
public IEnumerator GetEnumerator()
{
for (int i = _index - 1; i >= 0; i--)
yield return _arr[i];
}
}
You can then remove the _i variable, the MoveNext and Reset methods, as well as the Current property.
If you first want to make your existing code working, with the above note I made about nesting enumerators, you can change your GetEnumerator and Reset methods as follows:
public IEnumerator GetEnumerator()
{
Reset();
return this;
}
public void Reset() => _i = _index;
Note that you have to reset the _i variable to one step past the last (first) value as you're decrementing it inside MoveNext. If you don't, you'll ignore the last item added to the LIFO stack.
Your index counter variable using get values in _arr array (it is _i) and index varible that doing increase and decrease operations on it (it is _index) are different. Because of that your for loop never iterate your collection. I fix the code with some addition here. I hope it's helpful.
LIFO.cs
using System;
using System.Collections;
using System.Collections.Generic;
namespace TestApp
{
internal class LIFO : IEnumerator, IEnumerable
{
public LIFO(int size)
{
_arr = new char[size];
_index = 0;
}
public LIFO(char[] arr)
{
_arr = arr.Clone() as char[];
_index = 0;
}
public int Count() => _index;
public char Peek() => _index == 0 ? '\0' : _arr[_index - 1];
public bool Add(char c)
{
if (_index == _arr.Length)
return false;
try
{
_arr[_index] = c;
}
catch (Exception)
{
return false;
}
++_index;
_i = _index;
return true;
}
public void Pop()
{
if (_index == 0)
return;
_arr[--_index] = '\0';
_i = _index;
}
public IEnumerator GetEnumerator() => (IEnumerator)this;
public bool MoveNext() => --_index > -1;
public void Reset() => _index = _i;
public object Current
{
get => _arr[_index];
}
private int _index;
private int _i;
private readonly char[] _arr;
}
}
Program.cs
using System;
namespace TestApp
{
internal static class Program
{
private static void Main()
{
LIFO l = new LIFO(17);
l.Add('k');
l.Add('h');
l.Add('c');
Console.WriteLine("Count: " + l.Count());
foreach (var i in l)
Console.WriteLine(i);
l.Reset();
foreach (var i in l)
Console.WriteLine(i);
}
}
}
I am making a prototype application and for that I designed a class that behaves like an infinite looping list. That is, if my internal list contains 100 values, when I ask for the 101st value, I get the first, the 102nd yields the second, and so on, repeating.
So I would like to write the following code:
var slice = loopingListInstance.Skip(123).Take(5);
And for that I need to implement IEnumerable suitable, as I understand.
Here is my current code:
public class InfiniteLoopingList : IEnumerable<double>
{
double[] _values = File.ReadLines(#"c:\file.txt")
.Select(s => double.Parse(s, CultureInfo.InvariantCulture))
.ToArray();
int _size;
public InfiniteLoopingList()
{
_size = _values.Length;
}
public double this[int i]
{
get { return _values[i % _size]; }
set { _values[i % _size] = value; }
}
public IEnumerator<double> GetEnumerator()
{
return this.GetEnumerator();
}
IEnumerator IEnumerable.GetEnumerator()
{
// ???? now what ?? :(
}
}
Since you implemented the indexer property, you could do it via the simplest way as follows:
public IEnumerator<double> GetEnumerator()
{
int i = 0;
while (true)
yield return this[i++];
}
IEnumerator IEnumerable.GetEnumerator()
{
return GetEnumerator();
}
EDIT
Please notice, that this is not really infinite loop. This approach will only work until i = int.MaxValue. Thanks to #oleksii.
You don't need a class for this...
An extension method will do the trick:
public static class InfEx
{
public static IEnumerable<T> LoopForever<T>(this IEnumerable<T> src)
{
var data = new List<T>();
foreach(var item in src)
{
data.Add(item);
yield return item;
}
for(;;)
{
foreach(var item in data)
{
yield return item;
}
}
}
}
Now you can take a sequence and make it a looping, infinite sequence:
IEnumerable<Foo> mySeq = ...;
IEnumerable<Foo> infMySeq = mySeq.LoopForver();
IEnumerable<Foo> aSelectionOfInfMySeq = infMySeq.Skip(101).Take(5);
You can implement the IEnumerator interface:
class InifniteEnumerator<T> : IEnumerator<T> {
private int index = -1;
private IList<T> innerList;
private int repeatPos;
public InifniteEnumerator(IList<T> innerList, int repeatPos) {
this.innerList = innerList;
this.repeatPos = repeatPos;
}
public T Current {
get {
if (index == -1) {
throw new InvalidOperationException();
}
return this.innerList[index];
}
}
object IEnumerator.Current {
get {
return this.Current;
}
}
public void Dispose() {
}
public bool MoveNext() {
this.index++;
if (this.index == repeatPos) {
this.index = 0;
}
return true;
}
public void Reset() {
this.index = -1;
}
}
and then return an instance of it in the GetEnumerator methods:
IEnumerator IEnumerable.GetEnumerator() {
return this.GetEnumerator();
}
public IEnumerator<T> IEnumerable<T>.GetEnumerator() {
return new InifniteEnumerator(this, 100);
}
I've found myself in a position where I have to roll my own dynamic array implementation, due to various large performance benefits (in my case). However, after creating an enumerator for my version, and comparing the efficiency with the one List uses, I'm a bit bewildered; the List one is aproximately 30-40% faster than my version, even though it's much more complex.
Here's the important part of the List enumerator implementation:
public struct Enumerator : IEnumerator<T>, IDisposable, IEnumerator
{
private List<T> list;
private int index;
private int version;
private T current;
internal Enumerator(List<T> list)
{
this.list = list;
this.index = 0;
this.version = list._version;
this.current = default(T);
return;
}
public bool MoveNext()
{
List<T> list;
list = this.list;
if (this.version != list._version)
{
goto Label_004A;
}
if (this.index >= list._size)
{
goto Label_004A;
}
this.current = list._items[this.index];
this.index += 1;
return 1;
Label_004A:
return this.MoveNextRare();
}
public T Current
{
get { return this.current; }
}
}
And here's my very barebone version:
internal struct DynamicArrayEnumerator<T> : IEnumerator<T> where T : class
{
private readonly T[] internalArray;
private readonly int lastIndex;
private int currentIndex;
internal DynamicArrayEnumerator(DynamicArray<T> dynamicArray)
{
internalArray = dynamicArray.internalArray;
lastIndex = internalArray.Length - 1;
currentIndex = -1;
}
public T Current
{
get { return internalArray[currentIndex]; }
}
public bool MoveNext()
{
return (++currentIndex <= lastIndex);
}
}
I know this is micro-optimization, but I'm actually interested in understanding why the List enumerator is so much faster than mine. Any ideas? Thanks!
Edit:
As requested; the DynamicArray class (the relevant parts):
The enumerator is an inner class in this.
public struct DynamicArray<T> : IEnumerable<T> where T : class
{
private T[] internalArray;
private int itemCount;
internal T[] Data
{
get { return internalArray; }
}
public int Count
{
get { return itemCount; }
}
public DynamicArray(int count)
{
this.internalArray = new T[count];
this.itemCount = 0;
}
public IEnumerator<T> GetEnumerator()
{
return new DynamicArrayEnumerator<T>(this);
}
IEnumerator IEnumerable.GetEnumerator()
{
return this.GetEnumerator();
}
}
As for how I'm testing:
List<BaseClass> list = new List<BaseClass>(1000000);
DynamicArray<BaseClass> dynamicArray = new DynamicArray<BaseClass>(1000000);
// Code for filling with data omitted.
int numberOfRuns = 0;
float p1Total = 0;
float p2Total = 0;
while (numberOfRuns < 100)
{
PerformanceAnalyzer p1 = new PerformanceAnalyzer(() =>
{
int u = 0;
foreach (BaseClass b in list)
{
if (b.B > 100) // Some trivial task
u++;
}
});
p1.ExecuteAndClock();
p1Total += p1.TotalElapsedTicks;
PerformanceAnalyzer p2 = new PerformanceAnalyzer(() =>
{
int u = 0;
foreach (BaseClass b in dynamicArray)
{
if (b.B > 100) // Some trivial task
u++;
}
});
p2.ExecuteAndClock();
p2Total += p2.TotalElapsedTicks;
numberOfRuns++;
}
Console.WriteLine("List enumeration: " + p1Total / totalRuns + "\n");
Console.WriteLine("Dynamic array enumeration: " + p2Total / totalRuns + "\n");
The PerformanceAnalyzer class basically starts a Stopwatch, execute the supplied Action delegate, and then stop the Stopwatch afterwards.
Edit 2 (Quick answer to Ryan Gates):
There's a few reasons why I would want to roll my own, most importantly I need a very fast RemoveAt(int index) method.
Since I don't have to worry about the order of the list elements in my particular case, I can avoid the .Net built-in list's way of doing it:
public void RemoveAt(int index)
{
T local;
if (index < this._size)
{
goto Label_000E;
}
ThrowHelper.ThrowArgumentOutOfRangeException();
Label_000E:
this._size -= 1;
if (index >= this._size)
{
goto Label_0042;
}
Array.Copy(this._items, index + 1, this._items, index, this._size - index);
Label_0042:
this._items[this._size] = default(T);
this._version += 1;
return;
}
And instead using something along the lines of:
public void RemoveAt(int index)
{
// overwrites the element at the specified index with the last element in the array and decreases the item count.
internalArray[index] = internalArray[itemCount];
itemCount--;
}
Potencially saving enormous amounts of time in my case, if say the first 1000 elements in a long list have to be removed by index.
Okay, aside from benchmarking problems, here's how you can make your DynamicArray class more like List<T>:
public DynamicArrayEnumerator<T> GetEnumerator()
{
return new DynamicArrayEnumerator<T>(this);
}
IEnumerator<T> IEnumerable<T>.GetEnumerator()
{
return GetEnumerator();
}
IEnumerator IEnumerable.GetEnumerator()
{
return this.GetEnumerator();
}
Now, code which knows it's working with a dynamic array can iterate with a DynamicArrayEnumerator<T> without any boxing, and without virtual dispatch. This is exactly what List<T> does. The compiler notices when a type implements the pattern in a custom manner, and will use the types involved instead of the interfaces.
With your current code, you're getting no benefit from creating a struct - because you're boxing it in GetEnumerator().
Try the above change and fix the benchmark to work for longer. I'd expect to see a big difference.
I'm looking for a .Net implementation of a multiset. Can anyone recommend a good one?
(A multiset, or bag, is a set that can have duplicate values, and on which you can do set operations: intersection, difference, etc. A shopping cart for instance could be thought of as a multiset because you can have multiple occurrences of the same product.)
I do not know about one, however you could use a Dictionary for that, in which the value is the quantity of the item. And when the item is added for the second time, you vould increase the value for it in the dictionary.
An other possibility would be to simply use a List of items, in which you could put duplicates. This might be a better approach for a shopping cart.
Anything calling itself a C# implementation of a multiset should not be based on a Dictionary internally. Dictionaries are hash tables, unordered collections. C++'s sets, multisets, maps, and multimaps are ordered. Internally each is represented as some flavor of a self-balancing binary search tree.
In C# we should then use a SortedDictionary as the basis of our implementation as according to Microsoft's own documentation a SortedDictionary "is a binary search tree with O(log n) retrieval". A basic multiset can be implemented as follows:
public class SortedMultiSet<T> : IEnumerable<T>
{
private SortedDictionary<T, int> _dict;
public SortedMultiSet()
{
_dict = new SortedDictionary<T, int>();
}
public SortedMultiSet(IEnumerable<T> items) : this()
{
Add(items);
}
public bool Contains(T item)
{
return _dict.ContainsKey(item);
}
public void Add(T item)
{
if (_dict.ContainsKey(item))
_dict[item]++;
else
_dict[item] = 1;
}
public void Add(IEnumerable<T> items)
{
foreach (var item in items)
Add(item);
}
public void Remove(T item)
{
if (!_dict.ContainsKey(item))
throw new ArgumentException();
if (--_dict[item] == 0)
_dict.Remove(item);
}
// Return the last value in the multiset
public T Peek()
{
if (!_dict.Any())
throw new NullReferenceException();
return _dict.Last().Key;
}
// Return the last value in the multiset and remove it.
public T Pop()
{
T item = Peek();
Remove(item);
return item;
}
public IEnumerator<T> GetEnumerator()
{
foreach(var kvp in _dict)
for(int i = 0; i < kvp.Value; i++)
yield return kvp.Key;
}
IEnumerator IEnumerable.GetEnumerator()
{
return this.GetEnumerator();
}
}
Another option is to just wrap SortedSet, but instead of storing your type T in it, you store the value tuple (T value, int counter) where counter goes up by 1 with each new instance of value that is inserted. Essentially you're forcing the values to be distinct. You can efficiently use GetViewBetween() to find the largest value of counter for a particular value, then increment it to get the counter for a newly-added value. And unlike the count dictionary solution, you can use GetViewBetween() to replicate the functionality equal_range, lower_bound, and upper_bound gives in C++. Here is some code showing what I mean:
public class SortedMultiSet<T> : IEnumerable<T>
{
public void Add(T value)
{
var view = set.GetViewBetween((value, 0), (value, int.MaxValue));
int nextCounter = view.Count > 0 ? view.Max.counter + 1 : 0;
set.Add((value, nextCounter));
}
public bool RemoveOne(T value)
{
var view = set.GetViewBetween((value, 0), (value, int.MaxValue));
if (view.Count == 0) return false;
set.Remove(view.Max);
return true;
}
public bool RemoveAll(T value)
{
var view = set.GetViewBetween((value, 0), (value, int.MaxValue));
bool result = view.Count > 0;
view.Clear();
return result;
}
public SortedMultiSet<T> GetViewBetween(T min, T max)
{
var result = new SortedMultiSet<T>();
result.set = set.GetViewBetween((min, 0), (max, int.MaxValue));
return result;
}
public IEnumerator<T> GetEnumerator() =>
set.Select(x => x.value).GetEnumerator();
IEnumerator IEnumerable.GetEnumerator() =>
set.Select(x => x.value).GetEnumerator();
private SortedSet<(T value, int counter)> set =
new SortedSet<(T value, int counter)>();
}
Now you can write something like this:
var multiset = new SortedMultiSet<int>();
foreach (int i in new int[] { 1, 2, 2, 3, 4, 5, 5, 6, 7, 7, 8 })
{
multiset.Add(i);
}
foreach (int i in multiset.GetViewBetween(2, 7))
{
Console.Write(i + " "); // Output: 2 2 3 4 5 5 6 7 7
}
In the past, there were some issues where GetViewBetween() ran in time O(output size), rather than time O(log n), but I think those have been resolved. At the time it would count up nodes to cache the count, it now uses hierarchical counts to perform Count operations efficiently. See this StackOverflow post and this library code.
public class Multiset<T>: ICollection<T>
{
private readonly Dictionary<T, int> data;
public Multiset()
{
data = new Dictionary<T, int>();
}
private Multiset(Dictionary<T, int> data)
{
this.data = data;
}
public void Add(T item)
{
int count = 0;
data.TryGetValue(item, out count);
count++;
data[item] = count;
}
public void Clear()
{
data.Clear();
}
public Multiset<T> Except(Multiset<T> another)
{
Multiset<T> copy = new Multiset<T>(new Dictionary<T, int>(data));
foreach (KeyValuePair<T, int> kvp in another.data)
{
int count;
if (copy.data.TryGetValue(kvp.Key, out count))
{
if (count > kvp.Value)
{
copy.data[kvp.Key] = count - kvp.Value;
}
else
{
copy.data.Remove(kvp.Key);
}
}
}
return copy;
}
public Multiset<T> Intersection(Multiset<T> another)
{
Dictionary<T, int> newData = new Dictionary<T, int>();
foreach (T t in data.Keys.Intersect(another.data.Keys))
{
newData[t] = Math.Min(data[t], another.data[t]);
}
return new Multiset<T>(newData);
}
public bool Contains(T item)
{
return data.ContainsKey(item);
}
public void CopyTo(T[] array, int arrayIndex)
{
foreach (KeyValuePair<T, int> kvp in data)
{
for (int i = 0; i < kvp.Value; i++)
{
array[arrayIndex] = kvp.Key;
arrayIndex++;
}
}
}
public IEnumerable<T> Mode()
{
if (!data.Any())
{
return Enumerable.Empty<T>();
}
int modalFrequency = data.Values.Max();
return data.Where(kvp => kvp.Value == modalFrequency).Select(kvp => kvp.Key);
}
public int Count
{
get
{
return data.Values.Sum();
}
}
public bool IsReadOnly
{
get
{
return false;
}
}
public bool Remove(T item)
{
int count;
if (!data.TryGetValue(item, out count))
{
return false;
}
count--;
if (count == 0)
{
data.Remove(item);
}
else
{
data[item] = count;
}
return true;
}
public IEnumerator<T> GetEnumerator()
{
return new MultisetEnumerator<T>(this);
}
System.Collections.IEnumerator System.Collections.IEnumerable.GetEnumerator()
{
return new MultisetEnumerator<T>(this);
}
private class MultisetEnumerator<T> : IEnumerator<T>
{
public MultisetEnumerator(Multiset<T> multiset)
{
this.multiset = multiset;
baseEnumerator = multiset.data.GetEnumerator();
index = 0;
}
private readonly Multiset<T> multiset;
private readonly IEnumerator<KeyValuePair<T, int>> baseEnumerator;
private int index;
public T Current
{
get
{
return baseEnumerator.Current.Key;
}
}
public void Dispose()
{
baseEnumerator.Dispose();
}
object System.Collections.IEnumerator.Current
{
get
{
return baseEnumerator.Current.Key;
}
}
public bool MoveNext()
{
KeyValuePair<T, int> kvp = baseEnumerator.Current;
if (index < (kvp.Value - 1))
{
index++;
return true;
}
else
{
bool result = baseEnumerator.MoveNext();
index = 0;
return result;
}
}
public void Reset()
{
baseEnumerator.Reset();
}
}
}
You can use this implementation of a sorted multiset: SortedMultiSet.cs
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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?
You might like IntervalHeap from the C5 Generic Collection Library. To quote the user guide
Class IntervalHeap<T> implements interface IPriorityQueue<T> using an interval heap stored as an array of pairs. The FindMin and
FindMax operations, and the indexer’s get-accessor, take time O(1). The DeleteMin,
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'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));
}
}
I like using the OrderedBag and OrderedSet classes in PowerCollections as priority queues.
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++;
}
}
}
.NET 6+: As #rustyx commented, .NET 6 adds a System.Collections.Generic.PriorityQueue<TElement,TPriority> class. And FWIW it is open-source and implemented in c#.
Earlier .NET Core versions and .NET Framework: Microsoft has written (and shared online) 2 internal PriorityQueue classes within the .NET Framework. However, as #mathusum-mut commented, there is a bug in one of them (the SO community has, of course, provided fixes for it): Bug in Microsoft's internal PriorityQueue<T>?
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
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;
}
}
easy.
AlgoKit
I wrote an open source library called AlgoKit, available via NuGet. It contains:
Implicit d-ary heaps (ArrayHeap),
Binomial heaps,
Pairing heaps.
The code has been extensively tested. I definitely recommend you to give it a try.
Example
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);
Why those three heaps?
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.
Hint on choice
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.
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();
*/
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.
Here is the another implementation from NGenerics team:
NGenerics PriorityQueue
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; } }
}
}