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Posts tagged as “design”

花花酱 LeetCode 1865. Finding Pairs With a Certain Sum

You are given two integer arrays nums1 and nums2. You are tasked to implement a data structure that supports queries of two types:

  1. Add a positive integer to an element of a given index in the array nums2.
  2. Count the number of pairs (i, j) such that nums1[i] + nums2[j] equals a given value (0 <= i < nums1.length and 0 <= j < nums2.length).

Implement the FindSumPairs class:

  • FindSumPairs(int[] nums1, int[] nums2) Initializes the FindSumPairs object with two integer arrays nums1 and nums2.
  • void add(int index, int val) Adds val to nums2[index], i.e., apply nums2[index] += val.
  • int count(int tot) Returns the number of pairs (i, j) such that nums1[i] + nums2[j] == tot.

Example 1:

Input
["FindSumPairs", "count", "add", "count", "count", "add", "add", "count"]
[[[1, 1, 2, 2, 2, 3], [1, 4, 5, 2, 5, 4]], [7], [3, 2], [8], [4], [0, 1], [1, 1], [7]]
Output
[null, 8, null, 2, 1, null, null, 11]
Explanation
FindSumPairs findSumPairs = new FindSumPairs([1, 1, 2, 2, 2, 3], [1, 4, 5, 2, 5, 4]);
findSumPairs.count(7); // return 8; pairs (2,2), (3,2), (4,2), (2,4), (3,4), (4,4) make 2 + 5 and pairs (5,1), (5,5) make 3 + 4
findSumPairs.add(3, 2); // now nums2 = [1,4,5,4,5,4] 
findSumPairs.count(8); // return 2; pairs (5,2), (5,4) make 3 + 5 
findSumPairs.count(4); // return 1; pair (5,0) makes 3 + 1 
findSumPairs.add(0, 1); // now nums2 = [2,4,5,4,5,4] 
findSumPairs.add(1, 1); // now nums2 = [2,5,5,4,5,4] 
findSumPairs.count(7); // return 11; pairs (2,1), (2,2), (2,4), (3,1), (3,2), (3,4), (4,1), (4,2), (4,4) make 2 + 5 and pairs (5,3), (5,5) make 3 + 4

Constraints:

  • 1 <= nums1.length <= 1000
  • 1 <= nums2.length <= 105
  • 1 <= nums1[i] <= 109
  • 1 <= nums2[i] <= 105
  • 0 <= index < nums2.length
  • 1 <= val <= 105
  • 1 <= tot <= 109
  • At most 1000 calls are made to add and count each.

Solution: HashTable

Note nums1 and nums2 are unbalanced. Brute force method will take O(m*n) = O(103*105) = O(108) for each count call which will TLE. We could use a hashtable to store the counts of elements from nums2, and only iterate over nums1 to reduce the time complexity.

Time complexity:

init: O(m) + O(n)
add: O(1)
count: O(m)

Total time is less than O(106)

Space complexity: O(m + n)

C++

Python3

花花酱 LeetCode 1797. Design Authentication Manager

There is an authentication system that works with authentication tokens. For each session, the user will receive a new authentication token that will expire timeToLive seconds after the currentTime. If the token is renewed, the expiry time will be extended to expire timeToLive seconds after the (potentially different) currentTime.

Implement the AuthenticationManager class:

  • AuthenticationManager(int timeToLive) constructs the AuthenticationManager and sets the timeToLive.
  • generate(string tokenId, int currentTime) generates a new token with the given tokenId at the given currentTime in seconds.
  • renew(string tokenId, int currentTime) renews the unexpired token with the given tokenId at the given currentTime in seconds. If there are no unexpired tokens with the given tokenId, the request is ignored, and nothing happens.
  • countUnexpiredTokens(int currentTime) returns the number of unexpired tokens at the given currentTime.

Note that if a token expires at time t, and another action happens on time t (renew or countUnexpiredTokens), the expiration takes place before the other actions.

Example 1:

Input
["AuthenticationManager", "renew", "generate", "countUnexpiredTokens", "generate", "renew", "renew", "countUnexpiredTokens"]
[[5], ["aaa", 1], ["aaa", 2], [6], ["bbb", 7], ["aaa", 8], ["bbb", 10], [15]]
Output
[null, null, null, 1, null, null, null, 0]

Explanation AuthenticationManager authenticationManager = new AuthenticationManager(5); // Constructs the AuthenticationManager with timeToLive = 5 seconds. authenticationManager.renew(“aaa”, 1); // No token exists with tokenId “aaa” at time 1, so nothing happens. authenticationManager.generate(“aaa”, 2); // Generates a new token with tokenId “aaa” at time 2. authenticationManager.countUnexpiredTokens(6); // The token with tokenId “aaa” is the only unexpired one at time 6, so return 1. authenticationManager.generate(“bbb”, 7); // Generates a new token with tokenId “bbb” at time 7. authenticationManager.renew(“aaa”, 8); // The token with tokenId “aaa” expired at time 7, and 8 >= 7, so at time 8 the renew request is ignored, and nothing happens. authenticationManager.renew(“bbb”, 10); // The token with tokenId “bbb” is unexpired at time 10, so the renew request is fulfilled and now the token will expire at time 15. authenticationManager.countUnexpiredTokens(15); // The token with tokenId “bbb” expires at time 15, and the token with tokenId “aaa” expired at time 7, so currently no token is unexpired, so return 0.

Constraints:

  • 1 <= timeToLive <= 108
  • 1 <= currentTime <= 108
  • 1 <= tokenId.length <= 5
  • tokenId consists only of lowercase letters.
  • All calls to generate will contain unique values of tokenId.
  • The values of currentTime across all the function calls will be strictly increasing.
  • At most 2000 calls will be made to all functions combined.

Solution: Hashtable

Use a hashtable to store the token and its expiration time.

Time complexity: at most O(n) per operation
Space complexity: O(n)

C++

花花酱 LeetCode 1670. Design Front Middle Back Queue

Design a queue that supports push and pop operations in the front, middle, and back.

Implement the FrontMiddleBack class:

  • FrontMiddleBack() Initializes the queue.
  • void pushFront(int val) Adds val to the front of the queue.
  • void pushMiddle(int val) Adds val to the middle of the queue.
  • void pushBack(int val) Adds val to the back of the queue.
  • int popFront() Removes the front element of the queue and returns it. If the queue is empty, return -1.
  • int popMiddle() Removes the middle element of the queue and returns it. If the queue is empty, return -1.
  • int popBack() Removes the back element of the queue and returns it. If the queue is empty, return -1.

Notice that when there are two middle position choices, the operation is performed on the frontmost middle position choice. For example:

  • Pushing 6 into the middle of [1, 2, 3, 4, 5] results in [1, 2, 6, 3, 4, 5].
  • Popping the middle from [1, 2, 3, 4, 5, 6] returns 3 and results in [1, 2, 4, 5, 6].

Example 1:

Input:
["FrontMiddleBackQueue", "pushFront", "pushBack", "pushMiddle", "pushMiddle", "popFront", "popMiddle", "popMiddle", "popBack", "popFront"]
[[], [1], [2], [3], [4], [], [], [], [], []]
Output:
[null, null, null, null, null, 1, 3, 4, 2, -1]
Explanation:
FrontMiddleBackQueue q = new FrontMiddleBackQueue();
q.pushFront(1);   // [1]
q.pushBack(2);    // [1, 2]
q.pushMiddle(3);  // [1, 3, 2]
q.pushMiddle(4);  // [1, 4, 3, 2]
q.popFront();     // return 1 -> [4, 3, 2]
q.popMiddle();    // return 3 -> [4, 2]
q.popMiddle();    // return 4 -> [2]
q.popBack();      // return 2 -> []
q.popFront();     // return -1 -> [] (The queue is empty)

Constraints:

  • 1 <= val <= 109
  • At most 1000 calls will be made to pushFrontpushMiddlepushBackpopFrontpopMiddle, and popBack.

Solution: List + Middle Iterator

Time complexity: O(1) per op
Space complexity: O(n) in total

C++

花花酱 LeetCode 1656. Design an Ordered Stream

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"]
[[5], [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)

C++

Python3

花花酱 LeetCode 1206. Design Skiplist

Design a Skiplist without using any built-in libraries.

A Skiplist is a data structure that takes O(log(n)) time to adderase and search. Comparing with treap and red-black tree which has the same function and performance, the code length of Skiplist can be comparatively short and the idea behind Skiplists are just simple linked lists.

For example: we have a Skiplist containing [30,40,50,60,70,90] and we want to add 80 and 45 into it. The Skiplist works this way:


Artyom Kalinin [CC BY-SA 3.0], via Wikimedia Commons

You can see there are many layers in the Skiplist. Each layer is a sorted linked list. With the help of the top layers, add , erase and search can be faster than O(n). It can be proven that the average time complexity for each operation is O(log(n)) and space complexity is O(n).

To be specific, your design should include these functions:

  • bool search(int target) : Return whether the target exists in the Skiplist or not.
  • void add(int num): Insert a value into the SkipList. 
  • bool erase(int num): Remove a value in the Skiplist. If num does not exist in the Skiplist, do nothing and return false. If there exists multiple num values, removing any one of them is fine.

See more about Skiplist : https://en.wikipedia.org/wiki/Skip_list

Note that duplicates may exist in the Skiplist, your code needs to handle this situation.

Example:

Constraints:

  • 0 <= num, target <= 20000
  • At most 50000 calls will be made to searchadd, and erase.

Solution:

C++

Python3