# Posts published in “Tree”

Given the root of a binary tree, return the number of nodes where the value of the node is equal to the average of the values in its subtree.

Note:

• The average of n elements is the sum of the n elements divided by n and rounded down to the nearest integer.
• subtree of root is a tree consisting of root and all of its descendants.

Example 1:

Input: root = [4,8,5,0,1,null,6]
Output: 5
Explanation:
For the node with value 4: The average of its subtree is (4 + 8 + 5 + 0 + 1 + 6) / 6 = 24 / 6 = 4.
For the node with value 5: The average of its subtree is (5 + 6) / 2 = 11 / 2 = 5.
For the node with value 0: The average of its subtree is 0 / 1 = 0.
For the node with value 1: The average of its subtree is 1 / 1 = 1.
For the node with value 6: The average of its subtree is 6 / 1 = 6.


Example 2:

Input: root = 
Output: 1
Explanation: For the node with value 1: The average of its subtree is 1 / 1 = 1.


Constraints:

• The number of nodes in the tree is in the range [1, 1000].
• 0 <= Node.val <= 1000

## Solution: Recursion

Time complexity: O(n)
Space complexity: O(h)

## C++

You are given a 0-indexed 2D integer array flowers, where flowers[i] = [starti, endi] means the ith flower will be in full bloom from starti to endi (inclusive). You are also given a 0-indexed integer array persons of size n, where persons[i] is the time that the ith person will arrive to see the flowers.

Return an integer array answer of size n, where answer[i] is the number of flowers that are in full bloom when the ith person arrives.

Example 1:

Input: flowers = [[1,6],[3,7],[9,12],[4,13]], persons = [2,3,7,11]
Output: [1,2,2,2]
Explanation: The figure above shows the times when the flowers are in full bloom and when the people arrive.
For each person, we return the number of flowers in full bloom during their arrival.


Example 2:

Input: flowers = [[1,10],[3,3]], persons = [3,3,2]
Output: [2,2,1]
Explanation: The figure above shows the times when the flowers are in full bloom and when the people arrive.
For each person, we return the number of flowers in full bloom during their arrival.


Constraints:

• 1 <= flowers.length <= 5 * 104
• flowers[i].length == 2
• 1 <= starti <= endi <= 109
• 1 <= persons.length <= 5 * 104
• 1 <= persons[i] <= 109

## Solution: Prefix Sum + Binary Search

Use a treemap to store the counts (ordered by time t), when a flower begins to bloom at start, we increase m[start], when it dies at end, we decrease m[end+1]. prefix_sum[t] indicates the # of blooming flowers at time t.

For each people, use binary search to find the latest # of flowers before his arrival.

Time complexity: O(nlogn + mlogn)
Space complexity: O(n)

## C++

You are given the root of a binary tree that consists of exactly 3 nodes: the root, its left child, and its right child.

Return true if the value of the root is equal to the sum of the values of its two children, or false otherwise.

Example 1:

Input: root = [10,4,6]
Output: true
Explanation: The values of the root, its left child, and its right child are 10, 4, and 6, respectively.
10 is equal to 4 + 6, so we return true.


Example 2:

Input: root = [5,3,1]
Output: false
Explanation: The values of the root, its left child, and its right child are 5, 3, and 1, respectively.
5 is not equal to 3 + 1, so we return false.


Constraints:

• The tree consists only of the root, its left child, and its right child.
• -100 <= Node.val <= 100

## Solution:

Just want to check whether you know binary tree or not.

Time complexity: O(1)
Space complexity: O(1)

## C++

You are given a 2D integer array descriptions where descriptions[i] = [parenti, childi, isLefti] indicates that parenti is the parent of childi in a binary tree of unique values. Furthermore,

• If isLefti == 1, then childi is the left child of parenti.
• If isLefti == 0, then childi is the right child of parenti.

Construct the binary tree described by descriptions and return its root.

The test cases will be generated such that the binary tree is valid.

Example 1:

Input: descriptions = [[20,15,1],[20,17,0],[50,20,1],[50,80,0],[80,19,1]]
Output: [50,20,80,15,17,19]
Explanation: The root node is the node with value 50 since it has no parent.
The resulting binary tree is shown in the diagram.


Example 2:

Input: descriptions = [[1,2,1],[2,3,0],[3,4,1]]
Output: [1,2,null,null,3,4]
Explanation: The root node is the node with value 1 since it has no parent.
The resulting binary tree is shown in the diagram.


Constraints:

• 1 <= descriptions.length <= 104
• descriptions[i].length == 3
• 1 <= parenti, childi <= 105
• 0 <= isLefti <= 1
• The binary tree described by descriptions is valid.

## Solution: Hashtable + Recursion

1. Use one hashtable to track the children of each node.
2. Use another hashtable to track the parent of each node.
3. Find the root who doesn’t have parent.
4. Build the tree recursively from root.

Time complexity: O(n)
Space complexity: O(n)

## C++

Given the root of a complete binary tree, return the number of the nodes in the tree.

According to Wikipedia, every level, except possibly the last, is completely filled in a complete binary tree, and all nodes in the last level are as far left as possible. It can have between 1 and 2h nodes inclusive at the last level h.

Design an algorithm that runs in less than O(n) time complexity.

Example 1:

Input: root = [1,2,3,4,5,6]
Output: 6


Example 2:

Input: root = []
Output: 0


Example 3:

Input: root = 
Output: 1


Constraints:

• The number of nodes in the tree is in the range [0, 5 * 104].
• 0 <= Node.val <= 5 * 104
• The tree is guaranteed to be complete.

## Solution: Recursion

For each node, count the height of it’s left and right subtree by going left only.

Let L = height(left) R = height(root), if L == R, which means the left subtree is perfect.
It has (2^L – 1) nodes, +1 root, we only need to count nodes of right subtree recursively.
If L != R, L must be R + 1 since the tree is complete, which means the right subtree is perfect.
It has (2^(L-1) – 1) nodes, +1 root, we only need to count nodes of left subtree recursively.

Time complexity: T(n) = T(n/2) + O(logn) = O(logn*logn)

Space complexity: O(logn)