[英]Converting Directed Acyclic Graph (DAG) to tree
我正在尝试实现algoritm将Directed Acyclic Graph转换为Tree(为了好玩,学习,kata,命名它)。 所以我想出了数据结构Node:
/// <summary>
/// Represeting a node in DAG or Tree
/// </summary>
/// <typeparam name="T">Value of the node</typeparam>
public class Node<T>
{
/// <summary>
/// creats a node with no child nodes
/// </summary>
/// <param name="value">Value of the node</param>
public Node(T value)
{
Value = value;
ChildNodes = new List<Node<T>>();
}
/// <summary>
/// Creates a node with given value and copy the collection of child nodes
/// </summary>
/// <param name="value">value of the node</param>
/// <param name="childNodes">collection of child nodes</param>
public Node(T value, IEnumerable<Node<T>> childNodes)
{
if (childNodes == null)
{
throw new ArgumentNullException("childNodes");
}
ChildNodes = new List<Node<T>>(childNodes);
Value = value;
}
/// <summary>
/// Determines if the node has any child node
/// </summary>
/// <returns>true if has any</returns>
public bool HasChildNodes
{
get { return this.ChildNodes.Count != 0; }
}
/// <summary>
/// Travearse the Graph recursively
/// </summary>
/// <param name="root">root node</param>
/// <param name="visitor">visitor for each node</param>
public void Traverse(Node<T> root, Action<Node<T>> visitor)
{
if (root == null)
{
throw new ArgumentNullException("root");
}
if (visitor == null)
{
throw new ArgumentNullException("visitor");
}
visitor(root);
foreach (var node in root.ChildNodes)
{
Traverse(node, visitor);
}
}
/// <summary>
/// Value of the node
/// </summary>
public T Value { get; private set; }
/// <summary>
/// List of all child nodes
/// </summary>
public List<Node<T>> ChildNodes { get; private set; }
}
这很简单。 方法:
/// <summary>
/// Helper class for Node
/// </summary>
/// <typeparam name="T">Value of a node</typeparam>
public static class NodeHelper
{
/// <summary>
/// Converts Directed Acyclic Graph to Tree data structure using recursion.
/// </summary>
/// <param name="root">root of DAG</param>
/// <param name="seenNodes">keep track of child elements to find multiple connections (f.e. A connects with B and C and B also connects with C)</param>
/// <returns>root node of the tree</returns>
public static Node<T> DAG2TreeRec<T>(this Node<T> root, HashSet<Node<T>> seenNodes)
{
if (root == null)
{
throw new ArgumentNullException("root");
}
if (seenNodes == null)
{
throw new ArgumentNullException("seenNodes");
}
var length = root.ChildNodes.Count;
for (int i = 0; i < length; ++i)
{
var node = root.ChildNodes[i];
if (seenNodes.Contains(node))
{
var nodeClone = new Node<T>(node.Value, node.ChildNodes);
node = nodeClone;
}
else
{
seenNodes.Add(node);
}
DAG2TreeRec(node, seenNodes);
}
return root;
}
/// <summary>
/// Converts Directed Acyclic Graph to Tree data structure using explicite stack.
/// </summary>
/// <param name="root">root of DAG</param>
/// <param name="seenNodes">keep track of child elements to find multiple connections (f.e. A connects with B and C and B also connects with C)</param>
/// <returns>root node of the tree</returns>
public static Node<T> DAG2Tree<T>(this Node<T> root, HashSet<Node<T>> seenNodes)
{
if (root == null)
{
throw new ArgumentNullException("root");
}
if (seenNodes == null)
{
throw new ArgumentNullException("seenNodes");
}
var stack = new Stack<Node<T>>();
stack.Push(root);
while (stack.Count > 0)
{
var tempNode = stack.Pop();
var length = tempNode.ChildNodes.Count;
for (int i = 0; i < length; ++i)
{
var node = tempNode.ChildNodes[i];
if (seenNodes.Contains(node))
{
var nodeClone = new Node<T>(node.Value, node.ChildNodes);
node = nodeClone;
}
else
{
seenNodes.Add(node);
}
stack.Push(node);
}
}
return root;
}
}
并测试:
static void Main(string[] args)
{
// Jitter preheat
Dag2TreeTest();
Dag2TreeRecTest();
Console.WriteLine("Running time ");
Dag2TreeTest();
Dag2TreeRecTest();
Console.ReadKey();
}
public static void Dag2TreeTest()
{
HashSet<Node<int>> hashSet = new HashSet<Node<int>>();
Node<int> root = BulidDummyDAG();
Stopwatch stopwatch = new Stopwatch();
stopwatch.Start();
var treeNode = root.DAG2Tree<int>(hashSet);
stopwatch.Stop();
Console.WriteLine(string.Format("Dag 2 Tree = {0}ms",stopwatch.ElapsedMilliseconds));
}
private static Node<int> BulidDummyDAG()
{
Node<int> node2 = new Node<int>(2);
Node<int> node4 = new Node<int>(4);
Node<int> node3 = new Node<int>(3);
Node<int> node5 = new Node<int>(5);
Node<int> node6 = new Node<int>(6);
Node<int> node7 = new Node<int>(7);
Node<int> node8 = new Node<int>(8);
Node<int> node9 = new Node<int>(9);
Node<int> node10 = new Node<int>(10);
Node<int> root = new Node<int>(1);
//making DAG
root.ChildNodes.Add(node2);
root.ChildNodes.Add(node3);
node3.ChildNodes.Add(node2);
node3.ChildNodes.Add(node4);
root.ChildNodes.Add(node5);
node4.ChildNodes.Add(node6);
node4.ChildNodes.Add(node7);
node5.ChildNodes.Add(node8);
node2.ChildNodes.Add(node9);
node9.ChildNodes.Add(node8);
node9.ChildNodes.Add(node10);
var length = 10000;
Node<int> tempRoot = node10;
for (int i = 0; i < length; i++)
{
var nextChildNode = new Node<int>(11 + i);
tempRoot.ChildNodes.Add(nextChildNode);
tempRoot = nextChildNode;
}
return root;
}
public static void Dag2TreeRecTest()
{
HashSet<Node<int>> hashSet = new HashSet<Node<int>>();
Node<int> root = BulidDummyDAG();
Stopwatch stopwatch = new Stopwatch();
stopwatch.Start();
var treeNode = root.DAG2TreeRec<int>(hashSet);
stopwatch.Stop();
Console.WriteLine(string.Format("Dag 2 Tree Rec = {0}ms",stopwatch.ElapsedMilliseconds));
}
更重要的是,数据结构需要一些改进:
此外,在转换之前,需要检查certian thigs:
总而言之,它缩小为几个问题: 如何改善转换? 由于这是一次复发,因此可能会炸毁堆栈。 我可以添加堆栈来记住它。 如果我继续传递风格,我会更有效率吗?
我觉得在这种情况下不可变的数据结构会更好。 这是对的吗?
Childs是正确的名字吗? :)
算法:
如您所见,某些节点在输出中出现两次。 如果节点2有子节点,则整个子树将出现两次。 如果您希望每个节点只出现一次,请替换
if (hashSet.Contains(node)) { var nodeClone = new Node<T>(node.Value, node.Childs); node = nodeClone; }
同
if (hashSet.Contains(node)) { // node already seen -> do nothing }
我不会太担心堆栈的大小或递归的性能。 但是,您可以使用广度优先搜索替换深度优先搜索 ,这将导致节点更接近先前访问的根,从而产生更“自然”的树(在您的图片中,您已经按照BFS顺序编号了节点) )。
var seenNodes = new HashSet<Node>(); var q = new Queue<Node>(); q.Enqueue(root); seenNodes.Add(root); while (q.Count > 0) { var node = q.Dequeue(); foreach (var child in node.Childs) { if (!seenNodes.Contains(child )) { seenNodes.Add(child); q.Enqueue(child); } }
该算法处理钻石和周期。
多根
只需声明一个包含所有顶点的类Graph
class Graph { public List<Node> Nodes { get; private set; } public Graph() { Nodes = new List<Node>(); } }
码:
hashSet可以命名为seenNodes 。
代替
var length = root.Childs.Count; for (int i = 0; i < length; ++i) { var node = root.Childs[i];
写
foreach (var child in root.Childs)
在Traverse,访客是非常不必要的。 你可能宁愿有一个方法可以产生树的所有节点(以与遍历相同的顺序),并且用户可以对节点做任何事情:
foreach(var node in root.TraverseRecursive()) { Console.WriteLine(node.Value); }
如果重写GetHashCode和Equals,该算法将无法再区分具有相同值的两个不同节点,这可能不是您想要的。
我没有看到为什么LinkedList比List更好的原因,除了List在添加节点时所做的重新分配(容量2,4,8,16,...)。
你不必使用HashSet,你可以很容易地使用List>,因为这里只检查引用就足够了。 (因此不需要GetHashCode,Equals和运算符覆盖)
更简单的方法是序列化您的类,然后使用XmlSerializer将其再次反序列化为第二个对象。 序列化和反序列化时,引用2次的1个对象将成为具有不同引用的2个对象。
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