Design/Data-Structure

Problem The United States uses the imperial system of weights and measures, which means that there are many different, seemingly arbitrary units to measure distance. There are 12 inches in a foot, 3 feet in a yard, 22 yards in a chain, and so on. Create a data structure that can efficiently convert a certain quantity of one unit to the correct amount of any other unit. You should also allow for additional units to be added to the system. ...

Problem Design a time-based key-value data structure that can store multiple values for the same key at different time stamps and retrieve the key’s value at a certain timestamp. Implement the TimeMap class: TimeMap() Initializes the object of the data structure. void set(String key, String value, int timestamp) Stores the key key with the value value at the given time timestamp. String get(String key, int timestamp) Returns a value such that set was called previously, with timestamp_prev <= timestamp. If there are multiple such values, it returns the value associated with the largest timestamp_prev. If there are no values, it returns "". Examples Example 1: ...

Problem Design and implement a data structure for a Least Frequently Used (LFU) cache. Implement the LFUCache class: LFUCache(int capacity) Initializes the object with the capacity of the data structure. int get(int key) Gets the value of the key if the key exists in the cache. Otherwise, returns -1. void put(int key, int value) Update the value of the key if present, or inserts the key if not already present. When the cache reaches its capacity, it should invalidate and remove the least frequently used key before inserting a new item. For this problem, when there is a tie (i.e., two or more keys with the same frequency), the least recently used key would be invalidated. To determine the least frequently used key, a use counter is maintained for each key in the cache. The key with the smallest use counter is the least frequently used key. ...

Problem Design and implement a data structure for Least Recently Used (LRU) cache. It should support the following operations: get and add. Implement the LRUCache class: LRUCache(int capacity) Initialize the LRU cache with positive size capacity. int get(int key) Return the value of the key if the key exists, otherwise return -1. void put(int key, int value) Update the value of the key if the key exists. Otherwise, add the key-value pair to the cache. If the number of keys exceeds the capacity from this operation, evict the least recently used key. The functions get and put must each run in O(1) average time complexity. ...

Design HashMap Problem Problem Design a HashMap without using any built-in hash table libraries. Implement the MyHashMap class: MyHashMap() initializes the object with an empty map. void put(int key, int value) inserts a (key, value) pair into the HashMap. If the key already exists in the map, update the corresponding value. int get(int key) returns the value to which the specified key is mapped, or -1 if this map contains no mapping for the key. void remove(key) removes the key and its corresponding value if the map contains the mapping for the key. Examples Example 1: ...

Design Hashset Problem Problem Design a HashSet without using any built-in hash table libraries. Implement MyHashSet class: void add(key) Inserts the value key into the HashSet. bool contains(key) Returns whether the value key exists in the HashSet or not. void remove(key) Removes the value key in the HashSet. If key does not exist in the HashSet, do nothing. Examples Example 1: Input ["MyHashSet", "add", "add", "contains", "contains", "add", "contains", "remove", "contains"] [ [], [1], [2], [1], [3], [2], [2], [2], [2] ] Output [null, null, null, true, false, null, true, null, false] Explanation MyHashSet myHashSet = new MyHashSet(); myHashSet.add(1); // set = [1] myHashSet.add(2); // set = [1, 2] myHashSet.contains(1); // return True myHashSet.contains(3); // return False, (not found) myHashSet.add(2); // set = [1, 2] myHashSet.contains(2); // return True myHashSet.remove(2); // set = [1] myHashSet.contains(2); // return False, (already removed) ...