Certainly! Here's a comprehensive post about hashtable patterns on Leetcode Discuss:
Introduction:
Hashtables are a cornerstone data structure in computer science, enabling efficient data retrieval. In this post, we'll explore essential hashtable patterns and the thought process behind solving problems related to them.
1. Frequency Counting
Pattern Overview:
This pattern involves counting the frequency of elements in an array or a string using a hashtable. It's useful for finding duplicates or determining the most occurring element.
Example Problem:
Problem Statement: Leetcode Problem 1365 - How Many Numbers Are Smaller Than the Current Number
Brute Force Approach:
// Brute Force Approach
class Solution
{
public:
vector<int> smallerNumbersThanCurrent(vector<int> &nums)
{
vector<int> result;
for (auto i : nums)
{
int count = 0;
for (auto j : nums)
{
if (i > j)
count++;
}
result.push_back(count);
}
return result;
}
};
2. Pair Summing
Pattern Overview:
This pattern involves finding pairs of elements that sum up to a target value. It's useful for solving problems related to two-pointer techniques.
Example Problem:
Problem Statement: Leetcode Problem 1 - Two Sum
Brute Force Approach:
// Brute Force Approach
#include <unordered_map>
#include <vector>
using namespace std;
vector<int> twoSum(vector<int>& nums, int target) {
unordered_map<int, int> numToIndex;
for(int i = 0; i < nums.size(); i++) {
int complement = target - nums[i];
if(numToIndex.find(complement) != numToIndex.end()) {
return {numToIndex[complement], i};
}
numToIndex[nums[i]] = i;
}
return {};
}3. Subarray Sum
Pattern Overview:
This pattern involves finding subarrays whose elements sum up to a target value. It's crucial for solving problems related to dynamic programming and sliding window techniques.
Example Problem:
Problem Statement: Leetcode Problem 560 - Subarray Sum Equals K
Brute Force Approach:
// Brute Force Approach
#include <vector>
using namespace std;
int subarraySum(vector<int>& nums, int k) {
int count = 0;
for(int i = 0; i < nums.size(); i++) {
int sum = 0;
for(int j = i; j < nums.size(); j++) {
sum += nums[j];
if(sum == k) {
count++;
}
}
}
return count;
}Certainly! Here are a few more hashtable patterns along with example problems:
4. Anagram Grouping
Pattern Overview:
This pattern involves grouping anagrams together. It's useful for problems where you need to categorize words based on their character composition.
Example Problem:
Problem Statement: Leetcode Problem 49 - Group Anagrams
Brute Force Approach:
// Brute Force Approach
#include <unordered_map>
#include <vector>
using namespace std;
vector<vector<string>> groupAnagrams(vector<string>& strs) {
unordered_map<string, vector<string>> groups;
for(string str : strs) {
string key = str;
sort(key.begin(), key.end());
groups[key].push_back(str);
}
vector<vector<string>> result;
for(auto entry : groups) {
result.push_back(entry.second);
}
return result;
}5. Longest Consecutive Sequence
Pattern Overview:
This pattern involves finding the longest consecutive sequence of elements in an array. It's crucial for problems related to sequence and set operations.
Example Problem:
Problem Statement: Leetcode Problem 128 - Longest Consecutive Sequence
Brute Force Approach:
// Brute Force Approach
#include <unordered_set>
#include <vector>
using namespace std;
int longestConsecutive(vector<int>& nums) {
unordered_set<int> numSet(nums.begin(), nums.end());
int maxLen = 0;
for(int num : nums) {
if(!numSet.count(num-1)) {
int currNum = num;
int currStreak = 1;
while(numSet.count(currNum+1)) {
currNum++;
currStreak++;
}
maxLen = max(maxLen, currStreak);
}
}
return maxLen;
}6. Valid Sudoku
Pattern Overview:
This pattern involves determining whether a Sudoku board is valid. It's useful for problems that require validating the state of a game board.
Example Problem:
Problem Statement: Leetcode Problem 36 - Valid Sudoku
Brute Force Approach:
// Brute Force Approach
#include <vector>
using namespace std;
bool isValidSudoku(vector<vector<char>>& board) {
unordered_set<string> seen;
for(int i = 0; i < 9; i++) {
for(int j = 0; j < 9; j++) {
char curr = board[i][j];
if(curr != '.') {
string row = "r" + to_string(i) + curr;
string col = "c" + to_string(j) + curr;
string box = "b" + to_string(i/3) + to_string(j/3) + curr;
if(seen.count(row) || seen.count(col) || seen.count(box)) {
return false;
}
seen.insert(row);
seen.insert(col);
seen.insert(box);
}
}
}
return true;
}Certainly! Here are a few more hashtable patterns along with example problems:
7. Top K Elements
Pattern Overview:
This pattern involves finding the top k elements in a collection, based on some criteria. It's useful for problems that require finding the largest or smallest elements.
Example Problem:
Problem Statement: Leetcode Problem 347 - Top K Frequent Elements
Brute Force Approach:
// Brute Force Approach
#include <unordered_map>
#include <vector>
#include <queue>
using namespace std;
vector<int> topKFrequent(vector<int>& nums, int k) {
unordered_map<int, int> freq;
for(int num : nums) {
freq[num]++;
}
priority_queue<pair<int, int>> maxHeap;
for(auto entry : freq) {
maxHeap.push({entry.second, entry.first});
}
vector<int> result;
for(int i = 0; i < k; i++) {
result.push_back(maxHeap.top().second);
maxHeap.pop();
}
return result;
}8. Minimum Window Substring
Pattern Overview:
This pattern involves finding the minimum window in a string that contains all characters from another string. It's useful for substring and sliding window problems.
Example Problem:
Problem Statement: Leetcode Problem 76 - Minimum Window Substring
Brute Force Approach:
// Brute Force Approach
#include <unordered_map>
#include <string>
using namespace std;
string minWindow(string s, string t) {
unordered_map<char, int> required, window;
for(char ch : t) {
required[ch]++;
}
int left = 0, right = 0;
int minLength = INT_MAX;
int start = 0;
int requiredCount = t.size();
while(right < s.size()) {
char ch = s[right];
if(required.count(ch)) {
window[ch]++;
if(window[ch] <= required[ch]) {
requiredCount--;
}
}
right++;
while(requiredCount == 0) {
if(right - left < minLength) {
minLength = right - left;
start = left;
}
char leftChar = s[left];
if(required.count(leftChar)) {
window[leftChar]--;
if(window[leftChar] < required[leftChar]) {
requiredCount++;
}
}
left++;
}
}
return minLength == INT_MAX ? "" : s.substr(start, minLength);
}9. LRU Cache
Pattern Overview:
This pattern involves designing a data structure that supports get and put operations with constant time complexity. It's useful for cache implementation problems.
Example Problem:
Problem Statement: Leetcode Problem 146 - LRU Cache
Brute Force Approach:
// Brute Force Approach
#include <list>
#include <unordered_map>
using namespace std;
class LRUCache {
private:
int capacity;
list<pair<int, int>> cache;
unordered_map<int, list<pair<int, int>>::iterator> map;
void updateList(int key, int value) {
cache.erase(map[key]);
cache.push_front({key, value});
map[key] = cache.begin();
}
public:
LRUCache(int capacity) {
this->capacity = capacity;
}
int get(int key) {
if(map.find(key) != map.end()) {
int value = map[key]->second;
updateList(key, value);
return value;
}
return -1;
}
void put(int key, int value) {
if(map.find(key) != map.end()) {
updateList(key, value);
} else {
if(cache.size() >= capacity) {
int oldKey = cache.back().first;
cache.pop_back();
map.erase(oldKey);
}
cache.push_front({key, value});
map[key] = cache.begin();
}
}
};Conclusion:
These additional hashtable patterns expand your toolkit for solving a wide range of problems. Remember to practice and adapt these patterns to different scenarios you encounter.
Feel free to use and adapt the code examples provided based on specific problems you encounter. If you have any further questions or need additional assistance, feel free to ask. Happy coding!
