I am learning about bridges in graphs.
I have the following C# code (also available in a fiddle - https://dotnetfiddle.net/XQEEdy):
using System;
using System.Collections.Generic;
public class Program
{
public class Graph
{
private int[,] adjMatrix;
public Graph(int vertices)
{
adjMatrix = new int[vertices, vertices];
}
public int[,] AdjMatrix
{
get
{
return adjMatrix;
}
}
public void AddEdge(int source, int destination)
{
adjMatrix[source, destination] = 1;
adjMatrix[destination, source] = 1;
}
}
public static HashSet<Tuple<int, int>> Bridges(Graph graph)
{
var visited = new HashSet<int>();
var bridges = new HashSet<Tuple<int, int>>();
var ids = new Dictionary<int, int>();
var lowLinkValues = new Dictionary<int, int>();
var parent = -1;
var id = 0;
for (int i = 0; i < graph.AdjMatrix.GetLength(0); i++)
{
if (visited.Contains(i))
{
continue;
}
Dfs(i, parent, id, bridges, ids, lowLinkValues, visited, graph.AdjMatrix);
}
return bridges;
}
private static void Dfs(
int vertex,
int parent,
int id,
HashSet<Tuple<int, int>> bridges,
Dictionary<int, int> ids,
Dictionary<int, int> lowLinkValues,
HashSet<int> visited,
int[,] adjMatrix)
{
visited.Add(vertex);
ids.Add(vertex, id);
lowLinkValues.Add(vertex, id);
id++;
for (int i = 0; i < adjMatrix.GetLength(0); i++)
{
if (parent == i)
{
continue;
}
if (!visited.Contains(i))
{
parent = vertex;
Dfs(i, parent, id, bridges, ids, lowLinkValues, visited, adjMatrix);
if (ids[vertex] < lowLinkValues[i])
{
bridges.Add(Tuple.Create(vertex, i));
}
else
{
lowLinkValues[vertex] = Math.Min(lowLinkValues[vertex], lowLinkValues[i]);
}
}
else
{
lowLinkValues[vertex] = Math.Min(lowLinkValues[vertex], ids[i]);
}
}
}
public static void Main()
{
// Adjacency Matrix:
var g = new Graph(11);
g.AddEdge(0, 1);
g.AddEdge(0, 2);
g.AddEdge(0, 3);
g.AddEdge(0, 4);
g.AddEdge(4, 2);
g.AddEdge(3, 5);
g.AddEdge(4, 6);
g.AddEdge(6, 3);
g.AddEdge(6, 7);
g.AddEdge(6, 8);
g.AddEdge(7, 9);
g.AddEdge(9, 10);
g.AddEdge(8, 10);
// bridges should be: 0--1, 3--5
// but bridges collection is empty
var bridges = Bridges(g);
foreach (var bridge in bridges)
{
Console.WriteLine(bridge.Item1);
Console.WriteLine(bridge.Item2);
Console.WriteLine("\n");
}
}
}
I have compared the code to: https://github.com/williamfiset/Algorithms/blob/master/src/main/java/com/williamfiset/algorithms/graphtheory/BridgesAdjacencyList.java
I do not see any real differences, but I am still getting nothing returned. Drawing the graph out, it looks like edge(0,1) and edge(3,5) should be bridges - as removing the edges would mean 1 & 5 would be disconnected from the graph.
Similarly, if I use the same test case from the github link I also get no bridges returned.
I am clearly missing something, but I've not been able to pick up what it might be. Does anyone see what the issue with my code is?
The problem appears to be that you've not implemented an actual depth-first search, though that appears to be your intention with the name Dfs. A depth-first search starts at some vertex and follows all edges from that vertex in a depth-first manner (follows all edges from the first child before looking at the next child).
Your algorithm, however, looks at every node and simply defines the parent as the current node, so it's not really searching, it's just looking at every node in numerical order. A simplified version of your code (in pseudocode):
Dfs(Vertex current):
mark current as visited
for each Vertex v in the graph:
if v is not visited:
Dfs(v)
Note there's no relationship between the vertex called "current" and the vertices examined from the graph. Instead, the algorithm should be more like this:
Dfs(Vertex current, Vertex parent):
mark current as visited
for each Vertex v in the graph:
if v is not visited:
if v shares an edge with current:
Dfs(v)
I'll leave it as an exercise for you to figure out how to patch your algorithm to fix this issue. There may be other issues as well. I stopped once I found the first issue.
Let's say you wanted to implement a breadth-first search of a binary tree recursively. How would you go about it?
Is it possible using only the call-stack as auxiliary storage?
(I'm assuming that this is just some kind of thought exercise, or even a trick homework/interview question, but I suppose I could imagine some bizarre scenario where you're not allowed any heap space for some reason [some really bad custom memory manager? some bizarre runtime/OS issues?] while you still have access to the stack...)
Breadth-first traversal traditionally uses a queue, not a stack. The nature of a queue and a stack are pretty much opposite, so trying to use the call stack (which is a stack, hence the name) as the auxiliary storage (a queue) is pretty much doomed to failure, unless you're doing something stupidly ridiculous with the call stack that you shouldn't be.
On the same token, the nature of any non-tail recursion you try to implement is essentially adding a stack to the algorithm. This makes it no longer breadth first search on a binary tree, and thus the run-time and whatnot for traditional BFS no longer completely apply. Of course, you can always trivially turn any loop into a recursive call, but that's not any sort of meaningful recursion.
However, there are ways, as demonstrated by others, to implement something that follows the semantics of BFS at some cost. If the cost of comparison is expensive but node traversal is cheap, then as #Simon Buchan did, you can simply run an iterative depth-first search, only processing the leaves. This would mean no growing queue stored in the heap, just a local depth variable, and stacks being built up over and over on the call stack as the tree is traversed over and over again. And as #Patrick noted, a binary tree backed by an array is typically stored in breadth-first traversal order anyway, so a breadth-first search on that would be trivial, also without needing an auxiliary queue.
If you use an array to back the binary tree, you can determine the next node algebraically. if i is a node, then its children can be found at 2i + 1 (for the left node) and 2i + 2 (for the right node). A node's next neighbor is given by i + 1, unless i is a power of 2
Here's pseudocode for a very naive implementation of breadth first search on an array backed binary search tree. This assumes a fixed size array and therefore a fixed depth tree. It will look at parentless nodes, and could create an unmanageably large stack.
bintree-bfs(bintree, elt, i)
if (i == LENGTH)
return false
else if (bintree[i] == elt)
return true
else
return bintree-bfs(bintree, elt, i+1)
I couldn't find a way to do it completely recursive (without any auxiliary data-structure). But if the queue Q is passed by reference, then you can have the following silly tail recursive function:
BFS(Q)
{
if (|Q| > 0)
v <- Dequeue(Q)
Traverse(v)
foreach w in children(v)
Enqueue(Q, w)
BFS(Q)
}
The following method used a DFS algorithm to get all nodes in a particular depth - which is same as doing BFS for that level. If you find out depth of the tree and do this for all levels, the results will be same as a BFS.
public void PrintLevelNodes(Tree root, int level) {
if (root != null) {
if (level == 0) {
Console.Write(root.Data);
return;
}
PrintLevelNodes(root.Left, level - 1);
PrintLevelNodes(root.Right, level - 1);
}
}
for (int i = 0; i < depth; i++) {
PrintLevelNodes(root, i);
}
Finding depth of a tree is a piece of cake:
public int MaxDepth(Tree root) {
if (root == null) {
return 0;
} else {
return Math.Max(MaxDepth(root.Left), MaxDepth(root.Right)) + 1;
}
}
A simple BFS and DFS recursion in Java:
Just push/offer the root node of the tree in the stack/queue and call these functions.
public static void breadthFirstSearch(Queue queue) {
if (queue.isEmpty())
return;
Node node = (Node) queue.poll();
System.out.println(node + " ");
if (node.right != null)
queue.offer(node.right);
if (node.left != null)
queue.offer(node.left);
breadthFirstSearch(queue);
}
public static void depthFirstSearch(Stack stack) {
if (stack.isEmpty())
return;
Node node = (Node) stack.pop();
System.out.println(node + " ");
if (node.right != null)
stack.push(node.right);
if (node.left != null)
stack.push(node.left);
depthFirstSearch(stack);
}
Here is a BFS recursive traversal Python implementation, working for a graph with no cycle.
def bfs_recursive(level):
'''
#params level: List<Node> containing the node for a specific level.
'''
next_level = []
for node in level:
print(node.value)
for child_node in node.adjency_list:
next_level.append(child_node)
if len(next_level) != 0:
bfs_recursive(next_level)
class Node:
def __init__(self, value):
self.value = value
self.adjency_list = []
I would like to add my cents to the top answer in that if the language supports something like generator, bfs can be done co-recursively.
To begin with, #Tanzelax's answer reads:
Breadth-first traversal traditionally uses a queue, not a stack. The nature of a queue and a stack are pretty much opposite, so trying to use the call stack (which is a stack, hence the name) as the auxiliary storage (a queue) is pretty much doomed to failure
Indeed, ordinary function call's stack won't behave like a normal stack. But generator function will suspend the execution of function so it gives us the chance to yield next level of nodes' children without delving into deeper descendants of the node.
The following code is recursive bfs in Python.
def bfs(root):
yield root
for n in bfs(root):
for c in n.children:
yield c
The intuition here is:
bfs first will return the root as first result
suppose we already have the bfs sequence, the next level of elements in bfs is the immediate children of previous node in the sequence
repeat the above two procedures
I found a very beautiful recursive (even functional) Breadth-First traversal related algorithm. Not my idea, but i think it should be mentioned in this topic.
Chris Okasaki explains his breadth-first numbering algorithm from ICFP 2000 at http://okasaki.blogspot.de/2008/07/breadth-first-numbering-algorithm-in.html very clearly with only 3 pictures.
The Scala implementation of Debasish Ghosh, which i found at http://debasishg.blogspot.de/2008/09/breadth-first-numbering-okasakis.html, is:
trait Tree[+T]
case class Node[+T](data: T, left: Tree[T], right: Tree[T]) extends Tree[T]
case object E extends Tree[Nothing]
def bfsNumForest[T](i: Int, trees: Queue[Tree[T]]): Queue[Tree[Int]] = {
if (trees.isEmpty) Queue.Empty
else {
trees.dequeue match {
case (E, ts) =>
bfsNumForest(i, ts).enqueue[Tree[Int]](E)
case (Node(d, l, r), ts) =>
val q = ts.enqueue(l, r)
val qq = bfsNumForest(i+1, q)
val (bb, qqq) = qq.dequeue
val (aa, tss) = qqq.dequeue
tss.enqueue[org.dg.collection.BFSNumber.Tree[Int]](Node(i, aa, bb))
}
}
}
def bfsNumTree[T](t: Tree[T]): Tree[Int] = {
val q = Queue.Empty.enqueue[Tree[T]](t)
val qq = bfsNumForest(1, q)
qq.dequeue._1
}
The dumb way:
template<typename T>
struct Node { Node* left; Node* right; T value; };
template<typename T, typename P>
bool searchNodeDepth(Node<T>* node, Node<T>** result, int depth, P pred) {
if (!node) return false;
if (!depth) {
if (pred(node->value)) {
*result = node;
}
return true;
}
--depth;
searchNodeDepth(node->left, result, depth, pred);
if (!*result)
searchNodeDepth(node->right, result, depth, pred);
return true;
}
template<typename T, typename P>
Node<T>* searchNode(Node<T>* node, P pred) {
Node<T>* result = NULL;
int depth = 0;
while (searchNodeDepth(node, &result, depth, pred) && !result)
++depth;
return result;
}
int main()
{
// a c f
// b e
// d
Node<char*>
a = { NULL, NULL, "A" },
c = { NULL, NULL, "C" },
b = { &a, &c, "B" },
f = { NULL, NULL, "F" },
e = { NULL, &f, "E" },
d = { &b, &e, "D" };
Node<char*>* found = searchNode(&d, [](char* value) -> bool {
printf("%s\n", value);
return !strcmp((char*)value, "F");
});
printf("found: %s\n", found->value);
return 0;
}
Here is short Scala solution:
def bfs(nodes: List[Node]): List[Node] = {
if (nodes.nonEmpty) {
nodes ++ bfs(nodes.flatMap(_.children))
} else {
List.empty
}
}
Idea of using return value as accumulator is well suited.
Can be implemented in other languages in similar way, just make sure that your recursive function process list of nodes.
Test code listing (using #marco test tree):
import org.scalatest.FlatSpec
import scala.collection.mutable
class Node(val value: Int) {
private val _children: mutable.ArrayBuffer[Node] = mutable.ArrayBuffer.empty
def add(child: Node): Unit = _children += child
def children = _children.toList
override def toString: String = s"$value"
}
class BfsTestScala extends FlatSpec {
// 1
// / | \
// 2 3 4
// / | | \
// 5 6 7 8
// / | | \
// 9 10 11 12
def tree(): Node = {
val root = new Node(1)
root.add(new Node(2))
root.add(new Node(3))
root.add(new Node(4))
root.children(0).add(new Node(5))
root.children(0).add(new Node(6))
root.children(2).add(new Node(7))
root.children(2).add(new Node(8))
root.children(0).children(0).add(new Node(9))
root.children(0).children(0).add(new Node(10))
root.children(2).children(0).add(new Node(11))
root.children(2).children(0).add(new Node(12))
root
}
def bfs(nodes: List[Node]): List[Node] = {
if (nodes.nonEmpty) {
nodes ++ bfs(nodes.flatMap(_.children))
} else {
List.empty
}
}
"BFS" should "work" in {
println(bfs(List(tree())))
}
}
Output:
List(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12)
Here's a python implementation:
graph = {'A': ['B', 'C'],
'B': ['C', 'D'],
'C': ['D'],
'D': ['C'],
'E': ['F'],
'F': ['C']}
def bfs(paths, goal):
if not paths:
raise StopIteration
new_paths = []
for path in paths:
if path[-1] == goal:
yield path
last = path[-1]
for neighbor in graph[last]:
if neighbor not in path:
new_paths.append(path + [neighbor])
yield from bfs(new_paths, goal)
for path in bfs([['A']], 'D'):
print(path)
Here's a Scala 2.11.4 implementation of recursive BFS. I've sacrificed tail-call optimization for brevity, but the TCOd version is very similar. See also #snv's post.
import scala.collection.immutable.Queue
object RecursiveBfs {
def bfs[A](tree: Tree[A], target: A): Boolean = {
bfs(Queue(tree), target)
}
private def bfs[A](forest: Queue[Tree[A]], target: A): Boolean = {
forest.dequeueOption exists {
case (E, tail) => bfs(tail, target)
case (Node(value, _, _), _) if value == target => true
case (Node(_, l, r), tail) => bfs(tail.enqueue(List(l, r)), target)
}
}
sealed trait Tree[+A]
case class Node[+A](data: A, left: Tree[A], right: Tree[A]) extends Tree[A]
case object E extends Tree[Nothing]
}
The following seems pretty natural to me, using Haskell. Iterate recursively over levels of the tree (here I collect names into a big ordered string to show the path through the tree):
data Node = Node {name :: String, children :: [Node]}
aTree = Node "r" [Node "c1" [Node "gc1" [Node "ggc1" []], Node "gc2" []] , Node "c2" [Node "gc3" []], Node "c3" [] ]
breadthFirstOrder x = levelRecurser [x]
where levelRecurser level = if length level == 0
then ""
else concat [name node ++ " " | node <- level] ++ levelRecurser (concat [children node | node <- level])
I had to implement a heap traversal which outputs in a BFS order. It isn't actually BFS but accomplishes the same task.
private void getNodeValue(Node node, int index, int[] array) {
array[index] = node.value;
index = (index*2)+1;
Node left = node.leftNode;
if (left!=null) getNodeValue(left,index,array);
Node right = node.rightNode;
if (right!=null) getNodeValue(right,index+1,array);
}
public int[] getHeap() {
int[] nodes = new int[size];
getNodeValue(root,0,nodes);
return nodes;
}
Let v be the starting vertex
Let G be the graph in question
The following is the pseudo code without using queue
Initially label v as visited as you start from v
BFS(G,v)
for all adjacent vertices w of v in G:
if vertex w is not visited:
label w as visited
for all adjacent vertices w of v in G:
recursively call BFS(G,w)
BFS for a binary (or n-ary) tree can be done recursively without queues as follows (here in Java):
public class BreathFirst {
static class Node {
Node(int value) {
this(value, 0);
}
Node(int value, int nChildren) {
this.value = value;
this.children = new Node[nChildren];
}
int value;
Node[] children;
}
static void breathFirst(Node root, Consumer<? super Node> printer) {
boolean keepGoing = true;
for (int level = 0; keepGoing; level++) {
keepGoing = breathFirst(root, printer, level);
}
}
static boolean breathFirst(Node node, Consumer<? super Node> printer, int depth) {
if (depth < 0 || node == null) return false;
if (depth == 0) {
printer.accept(node);
return true;
}
boolean any = false;
for (final Node child : node.children) {
any |= breathFirst(child, printer, depth - 1);
}
return any;
}
}
An example traversal printing numbers 1-12 in ascending order:
public static void main(String... args) {
// 1
// / | \
// 2 3 4
// / | | \
// 5 6 7 8
// / | | \
// 9 10 11 12
Node root = new Node(1, 3);
root.children[0] = new Node(2, 2);
root.children[1] = new Node(3);
root.children[2] = new Node(4, 2);
root.children[0].children[0] = new Node(5, 2);
root.children[0].children[1] = new Node(6);
root.children[2].children[0] = new Node(7, 2);
root.children[2].children[1] = new Node(8);
root.children[0].children[0].children[0] = new Node(9);
root.children[0].children[0].children[1] = new Node(10);
root.children[2].children[0].children[0] = new Node(11);
root.children[2].children[0].children[1] = new Node(12);
breathFirst(root, n -> System.out.println(n.value));
}
#include <bits/stdc++.h>
using namespace std;
#define Max 1000
vector <int> adj[Max];
bool visited[Max];
void bfs_recursion_utils(queue<int>& Q) {
while(!Q.empty()) {
int u = Q.front();
visited[u] = true;
cout << u << endl;
Q.pop();
for(int i = 0; i < (int)adj[u].size(); ++i) {
int v = adj[u][i];
if(!visited[v])
Q.push(v), visited[v] = true;
}
bfs_recursion_utils(Q);
}
}
void bfs_recursion(int source, queue <int>& Q) {
memset(visited, false, sizeof visited);
Q.push(source);
bfs_recursion_utils(Q);
}
int main(void) {
queue <int> Q;
adj[1].push_back(2);
adj[1].push_back(3);
adj[1].push_back(4);
adj[2].push_back(5);
adj[2].push_back(6);
adj[3].push_back(7);
bfs_recursion(1, Q);
return 0;
}
Here is a JavaScript Implementation that fakes Breadth First Traversal with Depth First recursion. I'm storing the node values at each depth inside an array, inside of a hash. If a level already exists(we have a collision), so we just push to the array at that level. You could use an array instead of a JavaScript object as well since our levels are numeric and can serve as array indices. You can return nodes, values, convert to a Linked List, or whatever you want. I'm just returning values for the sake of simplicity.
BinarySearchTree.prototype.breadthFirstRec = function() {
var levels = {};
var traverse = function(current, depth) {
if (!current) return null;
if (!levels[depth]) levels[depth] = [current.value];
else levels[depth].push(current.value);
traverse(current.left, depth + 1);
traverse(current.right, depth + 1);
};
traverse(this.root, 0);
return levels;
};
var bst = new BinarySearchTree();
bst.add(20, 22, 8, 4, 12, 10, 14, 24);
console.log('Recursive Breadth First: ', bst.breadthFirstRec());
/*Recursive Breadth First:
{ '0': [ 20 ],
'1': [ 8, 22 ],
'2': [ 4, 12, 24 ],
'3': [ 10, 14 ] } */
Here is an example of actual Breadth First Traversal using an iterative approach.
BinarySearchTree.prototype.breadthFirst = function() {
var result = '',
queue = [],
current = this.root;
if (!current) return null;
queue.push(current);
while (current = queue.shift()) {
result += current.value + ' ';
current.left && queue.push(current.left);
current.right && queue.push(current.right);
}
return result;
};
console.log('Breadth First: ', bst.breadthFirst());
//Breadth First: 20 8 22 4 12 24 10 14
Following is my code for completely recursive implementation of breadth-first-search of a bidirectional graph without using loop and queue.
public class Graph
{
public int V;
public LinkedList<Integer> adj[];
Graph(int v)
{
V = v;
adj = new LinkedList[v];
for (int i=0; i<v; ++i)
adj[i] = new LinkedList<>();
}
void addEdge(int v,int w)
{
adj[v].add(w);
adj[w].add(v);
}
public LinkedList<Integer> getAdjVerted(int vertex)
{
return adj[vertex];
}
public String toString()
{
String s = "";
for (int i=0;i<adj.length;i++)
{
s = s +"\n"+i +"-->"+ adj[i] ;
}
return s;
}
}
//BFS IMPLEMENTATION
public static void recursiveBFS(Graph graph, int vertex,boolean visited[], boolean isAdjPrinted[])
{
if (!visited[vertex])
{
System.out.print(vertex +" ");
visited[vertex] = true;
}
if(!isAdjPrinted[vertex])
{
isAdjPrinted[vertex] = true;
List<Integer> adjList = graph.getAdjVerted(vertex);
printAdjecent(graph, adjList, visited, 0,isAdjPrinted);
}
}
public static void recursiveBFS(Graph graph, List<Integer> vertexList, boolean visited[], int i, boolean isAdjPrinted[])
{
if (i < vertexList.size())
{
recursiveBFS(graph, vertexList.get(i), visited, isAdjPrinted);
recursiveBFS(graph, vertexList, visited, i+1, isAdjPrinted);
}
}
public static void printAdjecent(Graph graph, List<Integer> list, boolean visited[], int i, boolean isAdjPrinted[])
{
if (i < list.size())
{
if (!visited[list.get(i)])
{
System.out.print(list.get(i)+" ");
visited[list.get(i)] = true;
}
printAdjecent(graph, list, visited, i+1, isAdjPrinted);
}
else
{
recursiveBFS(graph, list, visited, 0, isAdjPrinted);
}
}
C# implementation of recursive breadth-first search algorithm for a binary tree.
Binary tree data visualization
IDictionary<string, string[]> graph = new Dictionary<string, string[]> {
{"A", new [] {"B", "C"}},
{"B", new [] {"D", "E"}},
{"C", new [] {"F", "G"}},
{"E", new [] {"H"}}
};
void Main()
{
var pathFound = BreadthFirstSearch("A", "H", new string[0]);
Console.WriteLine(pathFound); // [A, B, E, H]
var pathNotFound = BreadthFirstSearch("A", "Z", new string[0]);
Console.WriteLine(pathNotFound); // []
}
IEnumerable<string> BreadthFirstSearch(string start, string end, IEnumerable<string> path)
{
if (start == end)
{
return path.Concat(new[] { end });
}
if (!graph.ContainsKey(start)) { return new string[0]; }
return graph[start].SelectMany(letter => BreadthFirstSearch(letter, end, path.Concat(new[] { start })));
}
If you want algorithm to work not only with binary-tree but with graphs what can have two and more nodes that points to same another node you must to avoid self-cycling by holding list of already visited nodes. Implementation may be looks like this.
Graph data visualization
IDictionary<string, string[]> graph = new Dictionary<string, string[]> {
{"A", new [] {"B", "C"}},
{"B", new [] {"D", "E"}},
{"C", new [] {"F", "G", "E"}},
{"E", new [] {"H"}}
};
void Main()
{
var pathFound = BreadthFirstSearch("A", "H", new string[0], new List<string>());
Console.WriteLine(pathFound); // [A, B, E, H]
var pathNotFound = BreadthFirstSearch("A", "Z", new string[0], new List<string>());
Console.WriteLine(pathNotFound); // []
}
IEnumerable<string> BreadthFirstSearch(string start, string end, IEnumerable<string> path, IList<string> visited)
{
if (start == end)
{
return path.Concat(new[] { end });
}
if (!graph.ContainsKey(start)) { return new string[0]; }
return graph[start].Aggregate(new string[0], (acc, letter) =>
{
if (visited.Contains(letter))
{
return acc;
}
visited.Add(letter);
var result = BreadthFirstSearch(letter, end, path.Concat(new[] { start }), visited);
return acc.Concat(result).ToArray();
});
}
I have made a program using c++ which is working in joint and disjoint graph too .
#include <queue>
#include "iostream"
#include "vector"
#include "queue"
using namespace std;
struct Edge {
int source,destination;
};
class Graph{
int V;
vector<vector<int>> adjList;
public:
Graph(vector<Edge> edges,int V){
this->V = V;
adjList.resize(V);
for(auto i : edges){
adjList[i.source].push_back(i.destination);
// adjList[i.destination].push_back(i.source);
}
}
void BFSRecursivelyJoinandDisjointtGraphUtil(vector<bool> &discovered, queue<int> &q);
void BFSRecursivelyJointandDisjointGraph(int s);
void printGraph();
};
void Graph :: printGraph()
{
for (int i = 0; i < this->adjList.size(); i++)
{
cout << i << " -- ";
for (int v : this->adjList[i])
cout <<"->"<< v << " ";
cout << endl;
}
}
void Graph ::BFSRecursivelyJoinandDisjointtGraphUtil(vector<bool> &discovered, queue<int> &q) {
if (q.empty())
return;
int v = q.front();
q.pop();
cout << v <<" ";
for (int u : this->adjList[v])
{
if (!discovered[u])
{
discovered[u] = true;
q.push(u);
}
}
BFSRecursivelyJoinandDisjointtGraphUtil(discovered, q);
}
void Graph ::BFSRecursivelyJointandDisjointGraph(int s) {
vector<bool> discovered(V, false);
queue<int> q;
for (int i = s; i < V; i++) {
if (discovered[i] == false)
{
discovered[i] = true;
q.push(i);
BFSRecursivelyJoinandDisjointtGraphUtil(discovered, q);
}
}
}
int main()
{
vector<Edge> edges =
{
{0, 1}, {0, 2}, {1, 2}, {2, 0}, {2,3},{3,3}
};
int V = 4;
Graph graph(edges, V);
// graph.printGraph();
graph.BFSRecursivelyJointandDisjointGraph(2);
cout << "\n";
edges = {
{0,4},{1,2},{1,3},{1,4},{2,3},{3,4}
};
Graph graph2(edges,5);
graph2.BFSRecursivelyJointandDisjointGraph(0);
return 0;
}
I think this can be done using pointers, without using any QUEUE.
Basically we are maintaining two pointers at any point, one is pointing to the parents, the other is pointing to the children to be processed ( linkedlist to all which have been processed )
Now you simply assign the pointer of the child & when parent processing finishes you just make the child to be parent for processing next level
following is my code :
//Tree Node
struct Node {
int val;
Node* left;
Node* right;
Node* next;
Node() : val(0), left(NULL), right(NULL), next(NULL) {}
Node(int _val) : val(_val), left(NULL), right(NULL), next(NULL) {}
Node(int _val, Node* _left, Node* _right, Node* _next)
: val(_val), left(_left), right(_right), next(_next) {}
};
//Algorightm :
void LevelTraverse(Node* parent,Node* chidstart,Node* childend ){
if(!parent && !chidstart) return; // we processed everything
if(!parent && chidstart){ //finished processing last level
parent=chidstart;chidstart=childend=NULL; // assgin child to parent for processing next level
LevelTraverse(parent,chidstart,childend);
}else if(parent && !chidstart){ // This is new level first node tobe processed
Node* temp=parent; parent=parent->next;
if(temp->left) { childend=chidstart=temp->left; }
if(chidstart){
if(temp->right) { childend->next=temp->right; childend=temp->right; }
}else{
if(temp->right) { childend=chidstart=temp->right; }
}
LevelTraverse(parent,chidstart,childend);
}else if(parent && chidstart){ //we are in mid of some level processing
Node* temp=parent; parent=parent->next;
if(temp->left) { childend->next=temp->left; childend=temp->left; }
if(temp->right) { childend->next=temp->right; childend=temp->right; }
LevelTraverse(parent,chidstart,childend);
}
}
//Driver code :
Node* connect(Node* root) {
if(!root) return NULL;
Node* parent; Node* childs, *childe; parent=childs=childe=NULL;
parent=root;
LevelTraverse(parent, childs, childe);
return root;
}
From an adaptation of this question while studying on AlgoExpert. The following Class is provided already in the prompt. Here are iterative and recursive solutions in python. The goal of this problem is to return an output array which lists the name of the nodes in order visited. So if the order of traversal was A -> B -> D -> F the output is ['A','B','D','F']
class Node:
def __init__(self, name):
self.children = []
self.name = name
def addChild(self, name):
self.children.append(Node(name))
return self
Recursive
def breadthFirstSearch(self, array):
return self._bfs(array, [self])
def _bfs(self, array, visited):
# Base case - no more nodes to visit
if len(visited) == 0:
return array
node = visited.pop(0)
array.append(node.name)
visited.extend(node.children)
return self._bfs(array, visited)
Iterative
def breadthFirstSearch(self, array):
array.append(self.name)
queue = [self]
while len(queue) > 0:
node = queue.pop(0)
for child in node.children:
array.append(child.name)
queue.append(child)
return array