# Posts published in “Hashtable”

Problem:

Design a data structure that supports all following operations in average O(1) time.

1. insert(val): Inserts an item val to the set if not already present.
2. remove(val): Removes an item val from the set if present.
3. getRandom: Returns a random element from current set of elements. Each element must have the same probability of being returned.

Idea:

Hashtable + array

Time complexity:

O(1)

Solution:

Related Problems:

Problem:

Design and implement a data structure for Least Frequently Used (LFU) cache. It should support the following operations: get and put.

get(key) – Get the value (will always be positive) of the key if the key exists in the cache, otherwise return -1.
put(key, value) – Set or insert the value if the key is not already present. When the cache reaches its capacity, it should invalidate the least frequently used item before inserting a new item. For the purpose of this problem, when there is a tie (i.e., two or more keys that have the same frequency), the least recently used key would be evicted.

Could you do both operations in O(1) time complexity?

Example:

Idea:
Hashtable + balanced search tree
Solution 1: O(logc) c is the capacity

Solution 2: O(1)

Problem:

Given a string, sort it in decreasing order based on the frequency of characters.

Example 1:

Input:
“tree”

Output:
“eert”

Explanation:
‘e’ appears twice while ‘r’ and ‘t’ both appear once.
So ‘e’ must appear before both ‘r’ and ‘t’. Therefore “eetr” is also a valid answer.
Example 2:

Input:
“cccaaa”

Output:
“cccaaa”

Explanation:
Both ‘c’ and ‘a’ appear three times, so “aaaccc” is also a valid answer.
Note that “cacaca” is incorrect, as the same characters must be together.
Example 3:

Input:
“Aabb”

Output:
“bbAa”

Explanation:
“bbaA” is also a valid answer, but “Aabb” is incorrect.
Note that ‘A’ and ‘a’ are treated as two different characters.

Idea:

Counting sort

Solution 1:

Sort the string based on element frequency

Solution 2:

Counting sort

Problem:

Design and implement a data structure for Least Recently Used (LRU) cache. It should support the following operations: get and put.

get(key) – Get the value (will always be positive) of the key if the key exists in the cache, otherwise return -1.
put(key, value) – Set or insert the value if the key is not already present. When the cache reached its capacity, it should invalidate the least recently used item before inserting a new item.

Could you do both operations in O(1) time complexity?

Example:

Idea:
Use Hashtable for fast mapping and double linked list for fast manipulation.
Time Complexity:
Put O(1)
Get O(1)
Solution:

Problem:

Implement a magic directory with buildDict, and search methods.

For the method buildDict, you’ll be given a list of non-repetitive words to build a dictionary.

For the method search, you’ll be given a word, and judge whether if you modify exactly one character into another character in this word, the modified word is in the dictionary you just built.

Example 1:

Note:

1. You may assume that all the inputs are consist of lowercase letters a-z.
2. For contest purpose, the test data is rather small by now. You could think about highly efficient algorithm after the contest.
3. Please remember to RESET your class variables declared in class MagicDictionary, as static/class variables are persisted across multiple test cases. Please see here for more details.
Idea:
Fuzzy match
Time Complexity:
buildDict: O(n*m)
n: numbers of words
m: length of word
search: O(m)
m: length of word
Space Complexity:
O(n*m)
Solution:

Java / Trie

Java / Trie v2

Mission News Theme by Compete Themes.