Related
I was asked this question, which I personally find hard:
Create a data structure that can:
Insert elements,
Remove elements,
Search Elements,
In time O(log n)
In addition,
It should have the following two functions which work in time O(1):
next(x):
given a pointer to the node of value x, return a pointer to the node with the smallest bigger value than x.
previous(x)
given a pointer to the node of value x, return a pointer to the node with the biggest smallest value than x.
If each node contains a pointer to its successor and a pointer to its predecessor, or equivalently - if you maintain both a doublely linked list and a tree, where each node in the tree points to its equivalent node in the list and vice versa - you'll get want you want. Updating the list on insert/delete is O(1) (after locating the closest node in the tree). Searching is performed on the tree. Succesor / predecessor are performed on the list.
#RogerLindsjö's idea from the comments is a good one. Essentially, keep a regular, balanced BST, then thread a doubly-linked list through the nodes keeping them in sorted order. That way, given a pointer to a node in the tree, you can find the largest value smaller than it or the smallest value greater than it simply by following the next or previous pointers in each node.
You can maintain this list through insertions and deletions without changing the overall runtime of an insert or delete. For example, here's how you might do an insertion of an element x:
Do a standard BST successor query to find the smallest value larger than x in the tree, and a standard BST predecessor query to find the largest value smaller than x in the tree. Each search takes time O(log n) to complete.
Do a regular BST insertion to insert x. Then, set its next and previous pointers to the two elements you found in the previous step, and update those nodes to point to your new node x. This also takes time O(log n).
The total time for the insertion is then O(log n), matching what a balanced tree can provide.
I'll leave it to you to figure out deletion, which can similarly maintain the linked list pointers without changing the overall cost of the operation.
Like most self-balancing trees, a B+ tree provides Insert, Remove, and Search operations with O(log n) time complexity.
In a B+ tree, a leaf node hosts multiple keys in an array, so the concept of "pointer to node with value x" does not really exist, but we could define it as the tuple (pointer, index), where the pointer is to the node, and index is the slot in which x is stored.
In a B+ tree the nodes at the bottom level contain all the keys, and these nodes are often linked, usually only in forward direction (i.e. to the right), but it is quite possible to also maintain a link in the opposite direction, without increasing the time complexity of the above operations.
With those two remarks in mind, prev-next operations can clearly be executed in O(1) time.
If your elements are integers you can use y-fast trie that supports all mentioned operations in O(log log m). Also, almost any search tree will allow doing these operations in O(log n) time by just going first up and then down (it will require a lot of concentration to not mess up with the order, though)
You can use two pointers in the node of the balanced tree, namely pred - predecessor and succ - successor. While inserting a node into the tree or deleting a node from the tree you just have to do some pointer manipulations, equivalent to those in doubly linked list.
The time complexity will be O(1) in each case.
I have provided my implementation for the insertion and deletion in case of AVL Tree below. The complete implementation is available here.
Structure of node
template<typename T>
struct node {
T key;
int freq;
node<T> *left;
node<T> *right;
node<T> *pred;
node<T> *succ;
int height;
node(T key): key(key), freq(1),
left(nullptr),
right(nullptr),
height(1),
pred(nullptr),
succ(nullptr) {}
};
insert function
node<T> *insert(node<T> *root, T key) {
if(root == nullptr)
return new node<T>(key);
if(!comp(key, root->key) && !comp(root->key, key)) {
++root->freq;
} else if(comp(key, root->key)) {
if(root->left == nullptr) {
node<T> *new_node = new node<T>(key);
/* set the pred and succ ptrs*/
new_node->succ = root;
new_node->pred = root->pred;
if(root->pred != nullptr)
root->pred->succ = new_node;
root->pred = new_node;
root->left = new_node;
} else {
root->left = insert(root->left, key);
}
} else {
if(root->right == nullptr) {
node<T> *new_node = new node<T>(key);
/* set the pred and succ ptrs*/
new_node->pred = root;
new_node->succ = root->succ;
if(root->succ != nullptr)
root->succ->pred = new_node;
root->succ = new_node;
root->right = new_node;
} else {
root->right = insert(root->right, key);
}
}
root->height = max(height(root->left), height(root->right)) + 1;
int bf = balance_factor(root);
node<T> *left = root->left;
node<T> *right = root->right;
if(bf > 1 && left != nullptr && comp(key, left->key)) {
/*
node was inserted at left subtree of left child
fix - right rotate root
*/
root = rotate_right(root);
} else if(bf > 1 && left != nullptr && comp(left->key, key)) {
/*
node was inserted at right subtree of left child
fix - left rotate left child
- right rotate root
*/
root->left = rotate_left(root->left);
root = rotate_right(root);
} else if(bf < -1 && right != nullptr && comp(right->key, key)) {
/*
node was inserted at right subtree of right child
fix - left rotate root
*/
root = rotate_left(root);
} else if(bf < -1 && right != nullptr && comp(key, right->key)) {
/*
node was inserted at left subtree of right child
fix - right rotate right child
- left roatate root
*/
root->right = rotate_right(root->right);
root = rotate_left(root);
}
return root;
}
erase function
node<T> *erase(node<T> *root, T key) {
if(root == nullptr)
return nullptr;
if(comp(key, root->key)) {
root->left = erase(root->left, key);
} else if(comp(root->key, key)) {
root->right = erase(root->right, key);
} else {
if(root->left == nullptr || root->right == nullptr) {
/* update pred and succ ptrs */
if(root->succ != nullptr)
root->succ->pred = root->pred;
if(root->pred != nullptr)
root->pred->succ = root->succ;
if(root->right == nullptr) {
node<T> *temp = root->left;
delete root;
root = temp;
} else {
node<T> *temp = root->right;
delete root;
root = temp;
}
} else {
// node<T> *succ = minVal(root->right);
root->key = root->succ->key;
root->freq = root->succ->freq;
root->right = erase(root->right, root->succ->key);
}
}
if(root != nullptr) {
root->height = max(height(root->left), height(root->right)) + 1;
int bf_root = balance_factor(root);
if(bf_root > 1) {
/*
case R
*/
int bf_left = balance_factor(root->left);
if(bf_left >= 0) {
/*
case R0 and R1
*/
root = rotate_right(root);
} else {
/*
case R -1
*/
root->left = rotate_left(root->left);
root = rotate_right(root);
}
} else if(bf_root < -1) {
/*
Case L
*/
int bf_right = balance_factor(root->right);
if(bf_right <= 0) {
/*
case L0 and L -1
*/
root = rotate_left(root);
} else {
/*
case L1
*/
root->right = rotate_right(root->right);
root = rotate_left(root);
}
}
}
return root;
}
How to construct a binary tree using a level order traversal sequence, for example from sequence {1,2,3,#,#,4,#,#,5}, we can construct a binary tree like this:
1
/ \
2 3
/
4
\
5
where '#' signifies a path terminator where no node exists below.
Finally I implement Pham Trung's algorithm by c++
struct TreeNode
{
TreeNode *left;
TreeNode *right;
int val;
TreeNode(int x): left(NULL), right(NULL), val(x) {}
};
TreeNode *build_tree(char nodes[], int n)
{
TreeNode *root = new TreeNode(nodes[0] - '0');
queue<TreeNode*> q;
bool is_left = true;
TreeNode *cur = NULL;
q.push(root);
for (int i = 1; i < n; i++) {
TreeNode *node = NULL;
if (nodes[i] != '#') {
node = new TreeNode(nodes[i] - '0');
q.push(node);
}
if (is_left) {
cur = q.front();
q.pop();
cur->left = node;
is_left = false;
} else {
cur->right = node;
is_left = true;
}
}
return root;
}
Assume using array int[]data with 0-based index, we have a simple function to get children:
Left child
int getLeftChild(int index){
if(index*2 + 1 >= data.length)
return -1;// -1 Means out of bound
return data[(index*2) + 1];
}
Right child
int getRightChild(int index){
if(index*2 + 2 >= data.length)
return -1;// -1 Means out of bound
return data[(index*2) + 2];
}
Edit:
Ok, so by maintaining a queue, we can build this binary tree.
We use a queue to maintain those nodes that are not yet processed.
Using a variable count to keep track of the number of children added for the current node.
First, create a root node, assign it as the current node.
So starting from index 1 (index 0 is the root), as the count is 0, we add this node as left child of the current node.
Increase count. If this node is not '#', add it to the queue.
Moving to the next index, the count is 1, so we add this as right child of current node, reset count to 0 and update current node (by assigning the current node as the first element in the queue). If this node is not '#', add it to the queue.
int count = 0;
Queue q = new Queue();
q.add(new Node(data[0]);
Node cur = null;
for(int i = 1; i < data.length; i++){
Node node = new Node(data[i]);
if(count == 0){
cur = q.dequeue();
}
if(count==0){
count++;
cur.leftChild = node;
}else {
count = 0;
cur.rightChild = node;
}
if(data[i] != '#'){
q.enqueue(node);
}
}
class Node{
int data;
Node leftChild, rightChild;
}
Note: this should only work for a binary tree and not BST.
we can build this binary tree from level order traversal by maintaining a queue. Queue is used to maintain those nodes that are not yet processed.
Using a variable count(index variable) to keep track of the number of children added for the current node.
First, create a root node, assign it as the current node. So starting from index 1,
index value is 1 means, we will add the next value as left node.
index value is 2 means we will add the next value as right node and index value 2 means that we have added left and right node, then do the same for the remaining nodes.
if arr value is -1
3.a. if index value is 1,i.e., there is no left node then change the index variable to add right node.
3.b. if index value is 2, i.e, there is no right node then we have repeat this step for the remaining.
static class Node{
int data;
Node left;
Node right;
Node(int d){
data=d;
left=null;
right=null;
}
}
public static Node constBT(int arr[],int n){
Node root=null;
Node curr=null;
int index=0;
Queue<Node> q=new LinkedList<>();
for(int i=0;i<n;i++){
if(root==null){
root=new Node(arr[i]);
q.add(root);
curr=q.peek();
index=1;
}else{
if(arr[i]==-1){
if(index==1)
index=2;
else{
q.remove();
curr=q.peek();
index=1;
}
}
else if(index==1){
curr.left=new Node(arr[i]);
q.add(curr.left);
index=2;
}else if(index==2){
curr.right=new Node(arr[i]);
q.add(curr.right);
q.remove();
curr=q.peek();
index=1;
}
}
}
return root;
}
My approach is similar to Pham Trung yet intutive. We would maintain an array of Nodes of given data instead of using a queue. We would do reverse engineering on BFS using queue. because BFS for a tree is basically its Level Order Traversal (LOT).
It is important to note that we should have the NULL childs of an node for the LOT to be unique and the reconstruction of Tree from LOT to be possible.
In this case LOT : 1,2,3,-1,-1,4,-1,-1,5
where I have used -1 instead of '#' to represent NULLs
And Tree is
1
/ \
2 3
/ \ /
-1 -1 4
/ \
-1 5
Here, we can easily see that when 1 is popped from the BFS queue, it pushed its left child
(2) and right child (3) in the queue. Similary, for 2 it pushed -1 (NULL) for both of its children. And the process is continued.
So, we can follow the following pseudo code to generate the tree rooted at LOT[0]
j = 1
For every node in LOT:
if n<=j: break
if node != NULL:
make LOT[j] left child of node
if n<=j+1: break
make LOT[j+1] right child of node
j <- j+2
Finally, C++ code for the same
Class Declaration and Preorder traversal
class Node{
public:
int val;
Node* lft, *rgt;
Node(int x ):val(x) {lft=rgt=nullptr;}
};
void preorder(Node* root) {
if(!root) return;
cout<<root->val<<" ";
preorder(root->lft);
preorder(root->rgt);
}
Restoring Tree from LOT Logic
int main(){
int arr[] = {1,2,3,-1,-1,4,-1,-1,5};
int n = sizeof(arr)/sizeof(int);
Node* brr[n];
for(int i=0;i<n;i++) {
if(arr[i]==-1) brr[i] = nullptr;
else brr[i] = new Node(arr[i]);
}
for(int i=0,j=1;j<n;i++) {
if(!brr[i]) continue;
brr[i]->lft = brr[j++];
if(j<n) brr[i]->rgt = brr[j++];
}
preorder(brr[0]);
}
Output: 1 2 3 4 5
Three types of tree traversals are inorder, preorder, and post order.
A fourth, less often used, traversal is level-order traversal. In a
level-order traveresal, all nodes at depth "d" are processed before
any node at depth d + 1. Level-order traversal differs from the other
traversals in that it is not done recursively; a queue is used,
instead of the implied stack of recursion.
My questions on above text snippet are
Why level order traversals are not done recursively?
How queue is used in level order traversal? Request clarification with Pseudo code will be helpful.
Thanks!
Level order traversal is actually a BFS, which is not recursive by nature. It uses Queue instead of Stack to hold the next vertices that should be opened. The reason for it is in this traversal, you want to open the nodes in a FIFO order, instead of a LIFO order, obtained by recursion
as I mentioned, the level order is actually a BFS, and its [BFS] pseudo code [taken from wikipedia] is:
1 procedure BFS(Graph,source):
2 create a queue Q
3 enqueue source onto Q
4 mark source
5 while Q is not empty:
6 dequeue an item from Q into v
7 for each edge e incident on v in Graph:
8 let w be the other end of e
9 if w is not marked:
10 mark w
11 enqueue w onto Q
(*) in a tree, marking the vertices is not needed, since you cannot get to the same node in 2 different paths.
void levelorder(Node *n)
{ queue < Node * >q;
q.push(n);
while(!q.empty())
{
Node *node = q.front();
cout<<node->value;
q.pop();
if(node->left != NULL)
q.push(node->left);
if (node->right != NULL)
q.push(node->right);
}
}
Instead of a queue, I used a map to solve this. Take a look, if you are interested. As I do a postorder traversal, I maintain the depth at which each node is positioned and use this depth as the key in a map to collect values in the same level
class Solution {
public:
map<int, vector<int> > levelValues;
void recursivePrint(TreeNode *root, int depth){
if(root == NULL)
return;
if(levelValues.count(root->val) == 0)
levelValues.insert(make_pair(depth, vector<int>()));
levelValues[depth].push_back(root->val);
recursivePrint(root->left, depth+1);
recursivePrint(root->right, depth+1);
}
vector<vector<int> > levelOrder(TreeNode *root) {
recursivePrint(root, 1);
vector<vector<int> > result;
for(map<int,vector<int> >::iterator it = levelValues.begin(); it!= levelValues.end(); ++it){
result.push_back(it->second);
}
return result;
}
};
The entire solution can be found here - http://ideone.com/zFMGKU
The solution returns a vector of vectors with each inner vector containing the elements in the tree in the correct order.
you can try solving it here - https://oj.leetcode.com/problems/binary-tree-level-order-traversal/
And, as you can see, we can also do this recursively in the same time and space complexity as the queue solution!
My questions on above text snippet are
Why level order traversals are not done recursively?
How queue is used in level order traversal? Request clarification with Pseudo code will be helpful.
I think it'd actually be easier to start with the second question. Once you understand the answer to the second question, you'll be better prepared to understand the answer to the first.
How level order traversal works
I think the best way to understand how level order traversal works is to go through the execution step by step, so let's do that.
We have a tree.
We want to traverse it level by level.
So, the order that we'd visit the nodes would be A B C D E F G.
To do this, we use a queue. Remember, queues are first in, first out (FIFO). I like to imagine that the nodes are waiting in line to be processed by an attendant.
Let's start by putting the first node A into the queue.
Ok. Buckle up. The setup is over. We're about to start diving in.
The first step is to take A out of the queue so it can be processed. But wait! Before we do so, let's put A's children, B and C, into the queue also.
Note: A isn't actually in the queue anymore at this point. I grayed it out to try to communicate this. If I removed it completely from the diagram, it'd make it harder to visualize what's happening later on in the story.
Note: A is being processed by the attendant at the desk in the diagram. In real life, processing a node can mean a lot of things. Using it to compute a sum, send an SMS, log to the console, etc, etc. Going off the metaphor in my diagram, you can tell the attendant how you want them to process the node.
Now we move on to the node that is next in line. In this case, B.
We do the same thing that we did with A: 1) add the children to the line, and 2) process the node.
Hey, check it out! It looks like what we're doing here is going to get us that level order traversal that we were looking for! Let's prove this to ourselves by continuing the step through.
Once we finish with B, C is next in line. We place C's children at the back of the line, and then process C.
Now let's see what happens next. D is next in line. D doesn't have any children, so we don't place anything at the back of the line. We just process D.
And then it's the same thing for E, F, and G.
Why it's not done recursively
Imagine what would happen if we used a stack instead of a queue. Let's rewind to the point where we had just visited A.
Here's how it'd look if we were using a stack.
Now, instead of going "in order", this new attendant likes to serve the most recent clients first, not the ones who have been waiting the longest. So C is who is up next, not B.
Here's where the key point is. Where the stack starts to cause a different processing order than we had with the queue.
Like before, we add C's children and then process C. We're just adding them to a stack instead of a queue this time.
Now, what's next? This new attendant likes to serve the most recent clients first (ie. we're using a stack), so G is up next.
I'll stop the execution here. The point is that something as simple as replacing the queue with a stack actually gives us a totally different execution order. I'd encourage you to finish the step through though.
You might be thinking: "Ok... but the question asked about recursion. What does this have to do with recursion?" Well, when you use recursion, something sneaky is going on. You never did anything with a stack data structure like s = new Stack(). However, the runtime uses the call stack. This ends up being conceptually similar to what I did above, and thus doesn't give us that A B C D E F G ordering we were looking for from level order traversal.
https://github.com/arun2pratap/data-structure/blob/master/src/main/java/com/ds/tree/binarytree/BinaryTree.java
for complete can look out for the above link.
public void levelOrderTreeTraversal(List<Node<T>> nodes){
if(nodes == null || nodes.isEmpty()){
return;
}
List<Node<T>> levelNodes = new ArrayList<>();
nodes.stream().forEach(node -> {
if(node != null) {
System.out.print(" " + node.value);
levelNodes.add(node.left);
levelNodes.add(node.right);
}
});
System.out.println("");
levelOrderTreeTraversal(levelNodes);
}
Also can check out
http://www.geeksforgeeks.org/
here you will find Almost all Data Structure related answers.
Level order traversal implemented by queue
# class TreeNode:
# def __init__(self, val=0, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
def levelOrder(root: TreeNode) -> List[List[int]]:
res = [] # store the node value
queue = [root]
while queue:
node = queue.pop()
# visit the node
res.append(node.val)
if node.left:
queue.insert(0, node.left)
if node.right:
queue.insert(0, node.right)
return res
Recursive implementation is also possible. However, it needs to know the max depth of the root in advance.
def levelOrder(root: TreeNode) -> List[int]:
res = []
max_depth = maxDepth(root)
for i in range(max_depth):
# level start from 0 to max_depth-1
visitLevel(root, i, action)
return res
def visitLevel(root:TreeNode, level:int, res: List):
if not root:
return
if level==0:
res.append(node.val)
else:
self.visitLevel(root.left, level-1, res)
self.visitLevel(root.right, level-1, res)
def maxDepth(root: TreeNode) -> int:
if not root:
return 0
if not root.left and not root.right:
return 1
return max([ maxDepth(root.left), maxDepth(root.right)]) + 1
For your point 1) we can use Java below code for level order traversal in recursive order, we have not used any library function for tree, all are user defined tree and tree specific functions -
class Node
{
int data;
Node left, right;
public Node(int item)
{
data = item;
left = right = null;
}
boolean isLeaf() { return left == null ? right == null : false; }
}
public class BinaryTree {
Node root;
Queue<Node> nodeQueue = new ConcurrentLinkedDeque<>();
public BinaryTree() {
root = null;
}
public static void main(String args[]) {
BinaryTree tree = new BinaryTree();
tree.root = new Node(1);
tree.root.left = new Node(2);
tree.root.right = new Node(3);
tree.root.left.left = new Node(4);
tree.root.left.right = new Node(5);
tree.root.right.left = new Node(6);
tree.root.right.right = new Node(7);
tree.root.right.left.left = new Node(8);
tree.root.right.left.right = new Node(9);
tree.printLevelOrder();
}
/*Level order traversal*/
void printLevelOrder() {
int h = height(root);
int i;
for (i = 1; i <= h; i++)
printGivenLevel(root, i);
System.out.println("\n");
}
void printGivenLevel(Node root, int level) {
if (root == null)
return;
if (level == 1)
System.out.print(root.data + " ");
else if (level > 1) {
printGivenLevel(root.left, level - 1);
printGivenLevel(root.right, level - 1);
}
}
/*Height of Binary tree*/
int height(Node root) {
if (root == null)
return 0;
else {
int lHeight = height(root.left);
int rHeight = height(root.right);
if (lHeight > rHeight)
return (lHeight + 1);
else return (rHeight + 1);
}
}
}
For your point 2) If you want to use non recursive function then you can use queue as below function-
public void levelOrder_traversal_nrec(Node node){
System.out.println("Level order traversal !!! ");
if(node == null){
System.out.println("Tree is empty");
return;
}
nodeQueue.add(node);
while (!nodeQueue.isEmpty()){
node = nodeQueue.remove();
System.out.printf("%s ",node.data);
if(node.left !=null)
nodeQueue.add(node.left);
if (node.right !=null)
nodeQueue.add(node.right);
}
System.out.println("\n");
}
Recursive Solution in C++
/**
* Definition for a binary tree node.
* struct TreeNode {
* int val;
* TreeNode *left;
* TreeNode *right;
* TreeNode() : val(0), left(nullptr), right(nullptr) {}
* TreeNode(int x) : val(x), left(nullptr), right(nullptr) {}
* TreeNode(int x, TreeNode *left, TreeNode *right) : val(x), left(left), right(right) {}
* };
*/
class Solution {
public:
vector<vector<int>> levels;
void helper(TreeNode* node,int level)
{
if(levels.size() == level) levels.push_back({});
levels[level].push_back(node->val);
if(node->left)
helper(node->left,level+1);
if(node->right)
helper(node->right,level+1);
}
vector<vector<int>> levelOrder(TreeNode* root) {
if(!root) return levels;
helper(root,0);
return levels;
}
};
We can use queue to solve this problem in less time complexity. Here is the solution of level order traversal suing Java.
class Solution {
public List<List<Integer>> levelOrder(TreeNode root) {
List<List<Integer>> levelOrderTraversal = new ArrayList<List<Integer>>();
List<Integer> currentLevel = new ArrayList<Integer>();
Queue<TreeNode> queue = new LinkedList<TreeNode>();
if(root != null)
{
queue.add(root);
queue.add(null);
}
while(!queue.isEmpty())
{
TreeNode queueRoot = queue.poll();
if(queueRoot != null)
{
currentLevel.add(queueRoot.val);
if(queueRoot.left != null)
{
queue.add(queueRoot.left);
}
if(queueRoot.right != null)
{
queue.add(queueRoot.right);
}
}
else
{
levelOrderTraversal.add(currentLevel);
if(!queue.isEmpty())
{
currentLevel = new ArrayList<Integer>();
queue.add(null);
}
}
}
return levelOrderTraversal;
}
}
How to count the number of right children in a binary tree?
This means that I only want the children marked as right.
Ex.
(Left | Right)
F(Root)
G | H
T U | I J
The right children would be U,H,and J.
What would be the algorithm to find these.
int count(Tree *r){
if(r == NULL) return 0;
int num_l=0, num_r=0;
if(r->left != NULL)
num_l = count(r->left);
if(r->right != NULL)
num_r = count(r->right)+1;
return num_l+num_r
}
In recursive approach,
You would be calling a function to traverse your tree,
for current node, you need to:
check if current node has right child (then increment the counter), and then call the function recursively for right node.
check if current node has left child, call the function recursively for left node.
This should work.
Do a simple traversal on the tree (i.e. post order, in order) and for each node do +1 if it has right child.
Example (didn't try to compile and check it):
int countRightChildren(Node root)
{
if (root == null) return 0;
int selfCount = (root.getRightChild() != null) ? 1 : 0;
return selfCount + countRightChildren(root.getLeftChild()) + countRightChildren(root.getRightChild());
}
You can do it recursively as:
If tree does not exist, there are no
R children.
If tree exists, then # R children = #
R children in R-subtree + # R
children in L-subtree
.
int countRChildren(Node *root) {
if(!root) // tree does not exist.
return 0;
// tree exists...now see if R node exits or not.
if(root->right) // right node exist
// return 1 + # of R children in L/R subtree.
return 1 + countRChildren(root->right) + countRChildren(root->left);
else // right nodes does not exist.
// total count of R children will come from left subtree.
return countRChildren(root->left);
}
This is include how i build the struct
struct Item
{
int info;
struct Item* right;
struct Item* left;
};
typedef struct Item* Node;
int countRightSons(Node tree)
{
if(!tree)
return 0;
if(tree->right != NULL)
return 1 + countRightSons(tree->right) + countRightSons(tree->left);
return countRightSons(tree->left);
}
Simple recursive approach,
check (even if not needed) for all the 4 possibilities:
left and right does not exists
left and right exists
left exists and right doesnt
right exists and left doesnt
public static int countRightChildren(BST tree) {
if (tree.root==null) return Integer.MIN_VALUE;
return countRightChildren(tree.root);}
public static int countRightChildren(Node curr) {
if (curr.right==null&&curr.left==null) return 0;
else if (curr.right!=null&&curr.left==null)
return curr.right.data+countRightChildren(curr.right);
else if (curr.right==null&&curr.left!=null)
return countRightChildren(curr.left);
else if (curr.right!=null&&curr.left!=null)
return curr.right.data+countRightChildren(curr.left)+countRightChildren(curr.right);
return Integer.MIN_VALUE;
}
Given a binary tree, how would you join the nodes at each level, left to right.
Say there are 5 nodes at level three, link all of them from left to right.
I don't need anybody to write code for this.. but just an efficient algorithm.
Thanks
Idea is:
1. Traverse tree with BFS.
2. When you do traversing, you're linking nodes on next level - if node has left and right node, you'll link left to right. If node has next node, then you link rightmost child of current node to leftmost child of next node.
public void BreadthFirstSearch(Action<Node> currentNodeAction)
{
Queue<Node> q = new Queue<Node>();
q.Enqueue(root);
while (q.Count != 0)
{
Node current = q.Dequeue();
if (currentNodeAction != null)
currentNodeAction(current);
if (current.left != null) q.Enqueue(current.left);
if (current.right != null) q.Enqueue(current.right);
}
}
private void Linker(Node node)
{
Link(node.left, node.right);
if (node.next != null)
Link(node.right ?? node.left, node.next.left ?? node.next.right);
}
private void Link(Node node1, Node node2)
{
if (node1 != null && node2 != null)
node1.next = node2;
}
public void LinkSameLevel()
{
BreadthFirstSearch(Linker);
}
Create a vector of linked lists.
Do a DFS keeping track of your level, and for each node you find, add it to the linked list of the level.
This will run in O(n) which is optimal.
Is this what you want to do?
This is not a direct answer to the question and may not be applicable based on your situation. But if you have control over the creation and maintenance of the binary tree, it would probably be more efficient to maintain the links while building/updating the tree.
If you kept both left and right pointers at each level, then it would be "simple" (always easy to say that word when someone else is doing the work) to maintain them. When inserting a new node at a given level, you know its direct siblings from the parent node information. You can adjust the left and right pointers for the three nodes involved (assuming not at the edge of the tree). Likewise, when removing a node, simply update the left and right pointers of the siblings of the node being removed. Change them to point to each other.
I agree with Thomas Ahle's answer if you want to make all of the row-lists at the same time. It seems that you are only interested in making the list for a one specific row.
Let's say you have a giant tree, but you only want to link the 5th row. There's clearly no point in accessing any node below the 5th row. So just do an early-terminated DFS. Unfortunately, you still have to run through all of the ancestors of every node in the list.
But here's the good news. If you have a perfect binary tree (where every single node branches exactly twice except for the last row) then the first row will have 1 one, the second 2, the third 4, the fourth 8 and the fifth 16. Thus there are more nodes on the last row (16) than all the previous put together (1 + 2 + 4 + 8 = 15), so searching through all of the ancestors is still just O(n), where n is the number of nodes in the row.
The worst case on the other hand would be to have the fifth row consist of a single node with a full binary tree above it. Then you still have to search through all 15 ancestors just to put that one node on the list.
So while this algorithm is really your only choice without modifying your data structure its efficiency relies entirely on how populated the row is compared to higher rows.
#include <queue>
struct Node {
Node *left;
Node *right;
Node *next;
};
/** Link all nodes of the same level in a binary tree. */
void link_level_nodes(Node *pRoot)
{
queue<Node*> q;
Node *prev; // Pointer to the revious node of the current level
Node *node;
int cnt; // Count of the nodes in the current level
int cntnext; // Count of the nodes in the next level
if(NULL == pRoot)
return;
q.push(pRoot);
cnt = 1;
cntnext = 0;
prev = NULL;
while (!q.empty()) {
node = q.front();
q.pop();
/* Add the left and the right nodes of the current node to the queue
and increment the counter of nodes at the next level.
*/
if (node->left){
q.push(node->left);
cntnext++;
}
if (node->right){
q.push(node->right);
cntnext++;
}
/* Link the previous node of the current level to this node */
if (prev)
prev->next = node;
/* Se the previous node to the current */
prev = node;
cnt--;
if (0 == cnt) { // if this is the last node of the current level
cnt = cntnext;
cntnext = 0;
prev = NULL;
}
}
}
What I usually do to solve this problem is that I do a simple inorder traversal.
I initialize my tree with a constructor that gives a level or column value to every node. Hence my head is at Level 0.
public Node(int d)
{
head=this;
data=d;
left=null;
right=null;
level=0;
}
Now, if in the traversal, I take a left or a right, I simply do the traversal with a level indicator. For each level identifier, I make a Linked List, possibly in a Vector of Nodes.
Different approaches can be used to solve this problem. Some of them that comes to mind are -
1) Using level order traversal or BFS.
We can modify queue entries to contain level of nodes.So queue node will contain a pointer to a tree node and an integer level. When we deque a node we can check the level of dequeued node if it is same we can set right pointer to point to it.
Time complexity for this method would be O(n).
2) If we have complete binary tree we can extend Pre-Order traversal. In this method we shall set right pointer of parent before the children.
Time complexity for this method would be O(n).
3) In case of incomplete binary tree we can modify method (2) by traversing first root then right pointer and then left so we can make sure that all nodes at level i have the right pointer set, before the level i+1 nodes.
Time complexity for this method would be O(n^2).
private class Node
{
public readonly Node Left;
public readonly Node Right;
public Node Link { get; private set; }
public void Run()
{
LinkNext = null;
}
private Node LinkNext
{
get
{
return Link == null ? null : (Link.Left ?? Link.Right ?? Link.LinkNext);
}
set
{
Link = value;
if (Right != null)
Right.LinkNext = LinkNext;
if (Left != null)
Left.LinkNext = Right ?? LinkNext;
}
}
}
Keep a depth array while breadth-first search.
vector<forward_list<index_t>> level_link(MAX_NODES);
index_t fringe_depth = 0;
static index_t depth[MAX_NODES];
memset(depth,0,sizeof(depth));
depth[0] = 0;
Now when the depth-changes while de-queuing, you get all linked !
explored[0] = true;
static deque<index_t> fringe;
fringe.clear();
fringe.push_back(0); // start bfs from node 0
while(!fringe.empty()) {
index_t xindex = fringe.front();
fringe.pop_front();
if(fringe_depth < depth[xindex]) {
// play with prev-level-data
fringe_depth = depth[xindex];
}
Now we have fringe-depth, so we can level-link.
level_link[fringe_depth].push_front(xindex);
for(auto yindex : nodes[xindex].connected) {
if(explored[yindex])
continue;
explored[yindex] = true;
depth[yindex] = depth[xindex] + 1;
fringe.push_back(yindex);
}
}