# Posts published in “Data Structure”

Design a system that manages the reservation state of n seats that are numbered from 1 to n.

Implement the SeatManager class:

• SeatManager(int n) Initializes a SeatManager object that will manage n seats numbered from 1 to n. All seats are initially available.
• int reserve() Fetches the smallest-numbered unreserved seat, reserves it, and returns its number.
• void unreserve(int seatNumber) Unreserves the seat with the given seatNumber.

Example 1:

Input
["SeatManager", "reserve", "reserve", "unreserve", "reserve", "reserve", "reserve", "reserve", "unreserve"]
[, [], [], , [], [], [], [], ]
Output

[null, 1, 2, null, 2, 3, 4, 5, null]

Explanation SeatManager seatManager = new SeatManager(5); // Initializes a SeatManager with 5 seats. seatManager.reserve(); // All seats are available, so return the lowest numbered seat, which is 1. seatManager.reserve(); // The available seats are [2,3,4,5], so return the lowest of them, which is 2. seatManager.unreserve(2); // Unreserve seat 2, so now the available seats are [2,3,4,5]. seatManager.reserve(); // The available seats are [2,3,4,5], so return the lowest of them, which is 2. seatManager.reserve(); // The available seats are [3,4,5], so return the lowest of them, which is 3. seatManager.reserve(); // The available seats are [4,5], so return the lowest of them, which is 4. seatManager.reserve(); // The only available seat is seat 5, so return 5. seatManager.unreserve(5); // Unreserve seat 5, so now the available seats are .


Constraints:

• 1 <= n <= 105
• 1 <= seatNumber <= n
• For each call to reserve, it is guaranteed that there will be at least one unreserved seat.
• For each call to unreserve, it is guaranteed that seatNumber will be reserved.
• At most 105 calls in total will be made to reserve and unreserve.

## Solution: TreeSet

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

## C++

You are given two integers, m and k, and a stream of integers. You are tasked to implement a data structure that calculates the MKAverage for the stream.

The MKAverage can be calculated using these steps:

1. If the number of the elements in the stream is less than m you should consider the MKAverage to be -1. Otherwise, copy the last m elements of the stream to a separate container.
2. Remove the smallest k elements and the largest k elements from the container.
3. Calculate the average value for the rest of the elements rounded down to the nearest integer.

Implement the MKAverage class:

• MKAverage(int m, int k) Initializes the MKAverage object with an empty stream and the two integers m and k.
• void addElement(int num) Inserts a new element num into the stream.
• int calculateMKAverage() Calculates and returns the MKAverage for the current stream rounded down to the nearest integer.

Example 1:

Input
[[3, 1], , , [], , [], , , , []]
Output
[null, null, null, -1, null, 3, null, null, null, 5]
Explanation MKAverage obj = new MKAverage(3, 1); obj.addElement(3); // current elements are  obj.addElement(1); // current elements are [3,1] obj.calculateMKAverage(); // return -1, because m = 3 and only 2 elements exist. obj.addElement(10); // current elements are [3,1,10] obj.calculateMKAverage(); // The last 3 elements are [3,1,10]. // After removing smallest and largest 1 element the container will be . // The average of  equals 3/1 = 3, return 3 obj.addElement(5); // current elements are [3,1,10,5] obj.addElement(5); // current elements are [3,1,10,5,5] obj.addElement(5); // current elements are [3,1,10,5,5,5] obj.calculateMKAverage(); // The last 3 elements are [5,5,5]. // After removing smallest and largest 1 element the container will be . // The average of  equals 5/1 = 5, return 5


Constraints:

• 3 <= m <= 105
• 1 <= k*2 < m
• 1 <= num <= 105
• At most 105 calls will be made to addElement and calculateMKAverage.

## Solution 1: Multiset * 3

Use three multiset to track the left part (smallest k elements), right part (largest k elements) and mid (middle part of m – 2*k elements).

Time complexity: addElememt: O(logn), average: O(1)
Space complexity: O(n)

## C++

There are n (id, value) pairs, where id is an integer between 1 and n and value is a string. No two pairs have the same id.

Design a stream that takes the n pairs in an arbitrary order, and returns the values over several calls in increasing order of their ids.

Implement the OrderedStream class:

• OrderedStream(int n) Constructs the stream to take n values and sets a current ptr to 1.
• String[] insert(int id, String value) Stores the new (id, value) pair in the stream. After storing the pair:
• If the stream has stored a pair with id = ptr, then find the longest contiguous incrementing sequence of ids starting with id = ptr and return a list of the values associated with those ids in order. Then, update ptr to the last id + 1.
• Otherwise, return an empty list.

Example:

Input
["OrderedStream", "insert", "insert", "insert", "insert", "insert"]
[, [3, "ccccc"], [1, "aaaaa"], [2, "bbbbb"], [5, "eeeee"], [4, "ddddd"]]
Output
[null, [], ["aaaaa"], ["bbbbb", "ccccc"], [], ["ddddd", "eeeee"]]
Explanation
OrderedStream os= new OrderedStream(5);
os.insert(3, "ccccc"); // Inserts (3, "ccccc"), returns [].
os.insert(1, "aaaaa"); // Inserts (1, "aaaaa"), returns ["aaaaa"].
os.insert(2, "bbbbb"); // Inserts (2, "bbbbb"), returns ["bbbbb", "ccccc"].
os.insert(5, "eeeee"); // Inserts (5, "eeeee"), returns [].
os.insert(4, "ddddd"); // Inserts (4, "ddddd"), returns ["ddddd", "eeeee"].


## Solution: Straight Forward

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

## Python3

Implement a SnapshotArray that supports the following interface:

• SnapshotArray(int length) initializes an array-like data structure with the given length.  Initially, each element equals 0.
• void set(index, val) sets the element at the given index to be equal to val.
• int snap() takes a snapshot of the array and returns the snap_id: the total number of times we called snap() minus 1.
• int get(index, snap_id) returns the value at the given index, at the time we took the snapshot with the given snap_id

Example 1:

Input: ["SnapshotArray","set","snap","set","get"]
[,[0,5],[],[0,6],[0,0]]
Output: [null,null,0,null,5]
Explanation:
SnapshotArray snapshotArr = new SnapshotArray(3); // set the length to be 3
snapshotArr.set(0,5);  // Set array = 5
snapshotArr.snap();  // Take a snapshot, return snap_id = 0
snapshotArr.set(0,6);
snapshotArr.get(0,0);  // Get the value of array with snap_id = 0, return 5

Constraints:

• 1 <= length <= 50000
• At most 50000 calls will be made to setsnap, and get.
• 0 <= index < length
• 0 <= snap_id < (the total number of times we call snap())
• 0 <= val <= 10^9

## Solution: map + upper_bound

Use a vector to store maps, one map per element.
The map stores {snap_id -> val}, use upper_bound to find the first version > snap_id and use previous version’s value.

Time complexity:
Set: O(log|snap_id|)
Get: O(log|snap_id|)
Snap: O(1)
Space complexity: O(length + set_calls)

# Problem

Design your implementation of the circular double-ended queue (deque).
Your implementation should support following operations:

• MyCircularDeque(k): Constructor, set the size of the deque to be k.
• insertFront(): Adds an item at the front of Deque. Return true if the operation is successful.
• insertLast(): Adds an item at the rear of Deque. Return true if the operation is successful.
• deleteFront(): Deletes an item from the front of Deque. Return true if the operation is successful.
• deleteLast(): Deletes an item from the rear of Deque. Return true if the operation is successful.
• getFront(): Gets the front item from the Deque. If the deque is empty, return -1.
• getRear(): Gets the last item from Deque. If the deque is empty, return -1.
• isEmpty(): Checks whether Deque is empty or not.
• isFull(): Checks whether Deque is full or not.

Example:

MyCircularDeque circularDeque = new MycircularDeque(3); // set the size to be 3
circularDeque.insertLast(1);			// return true
circularDeque.insertLast(2);			// return true
circularDeque.insertFront(3);			// return true
circularDeque.insertFront(4);			// return false, the queue is full
circularDeque.getRear();  				// return 32
circularDeque.isFull();				// return true
circularDeque.deleteLast();			// return true
circularDeque.insertFront(4);			// return true
circularDeque.getFront();				// return 4


Note:

• All values will be in the range of [1, 1000].
• The number of operations will be in the range of [1, 1000].
• Please do not use the built-in Deque library.

# Solution

Using head and tail to pointer to the head and the tail in the circular buffer.