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Preface
- FAQ
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Part I - Basics
- Basics Data Structure
- Basics Sorting
- Basics Algorithm
- Basics Misc
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Part II - Coding
- String
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Integer Array
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Remove Element
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Zero Sum Subarray
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Subarray Sum K
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Subarray Sum Closest
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Recover Rotated Sorted Array
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Product of Array Exclude Itself
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Partition Array
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First Missing Positive
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2 Sum
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3 Sum
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3 Sum Closest
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Remove Duplicates from Sorted Array
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Remove Duplicates from Sorted Array II
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Merge Sorted Array
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Merge Sorted Array II
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Median
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Partition Array by Odd and Even
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Kth Largest Element
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Remove Element
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Binary Search
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First Position of Target
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Search Insert Position
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Search for a Range
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First Bad Version
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Search a 2D Matrix
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Search a 2D Matrix II
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Find Peak Element
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Search in Rotated Sorted Array
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Search in Rotated Sorted Array II
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Find Minimum in Rotated Sorted Array
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Find Minimum in Rotated Sorted Array II
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Median of two Sorted Arrays
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Sqrt x
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Wood Cut
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First Position of Target
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Math and Bit Manipulation
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Single Number
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Single Number II
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Single Number III
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O1 Check Power of 2
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Convert Integer A to Integer B
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Factorial Trailing Zeroes
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Unique Binary Search Trees
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Update Bits
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Fast Power
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Hash Function
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Happy Number
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Count 1 in Binary
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Fibonacci
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A plus B Problem
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Print Numbers by Recursion
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Majority Number
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Majority Number II
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Majority Number III
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Digit Counts
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Ugly Number
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Plus One
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Palindrome Number
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Task Scheduler
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Single Number
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Linked List
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Remove Duplicates from Sorted List
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Remove Duplicates from Sorted List II
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Remove Duplicates from Unsorted List
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Partition List
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Add Two Numbers
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Two Lists Sum Advanced
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Remove Nth Node From End of List
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Linked List Cycle
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Linked List Cycle II
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Reverse Linked List
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Reverse Linked List II
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Merge Two Sorted Lists
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Merge k Sorted Lists
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Reorder List
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Copy List with Random Pointer
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Sort List
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Insertion Sort List
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Palindrome Linked List
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LRU Cache
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Rotate List
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Swap Nodes in Pairs
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Remove Linked List Elements
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Remove Duplicates from Sorted List
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Binary Tree
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Binary Tree Preorder Traversal
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Binary Tree Inorder Traversal
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Binary Tree Postorder Traversal
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Binary Tree Level Order Traversal
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Binary Tree Level Order Traversal II
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Maximum Depth of Binary Tree
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Balanced Binary Tree
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Binary Tree Maximum Path Sum
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Lowest Common Ancestor
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Invert Binary Tree
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Diameter of a Binary Tree
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Construct Binary Tree from Preorder and Inorder Traversal
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Construct Binary Tree from Inorder and Postorder Traversal
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Subtree
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Binary Tree Zigzag Level Order Traversal
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Binary Tree Serialization
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Binary Tree Preorder Traversal
- Binary Search Tree
- Exhaustive Search
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Dynamic Programming
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Triangle
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Backpack
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Backpack II
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Minimum Path Sum
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Unique Paths
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Unique Paths II
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Climbing Stairs
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Jump Game
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Word Break
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Longest Increasing Subsequence
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Palindrome Partitioning II
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Longest Common Subsequence
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Edit Distance
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Jump Game II
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Best Time to Buy and Sell Stock
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Best Time to Buy and Sell Stock II
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Best Time to Buy and Sell Stock III
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Best Time to Buy and Sell Stock IV
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Distinct Subsequences
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Interleaving String
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Maximum Subarray
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Maximum Subarray II
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Longest Increasing Continuous subsequence
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Longest Increasing Continuous subsequence II
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Maximal Square
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Triangle
- Graph
- Data Structure
- Big Data
- Problem Misc
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Part III - Contest
- Google APAC
- Microsoft
- Appendix I Interview and Resume
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Tags
Group Anagrams
Tags: Hash Table, String, Medium
Question
- leetcode: Group Anagrams
- lintcode: Group Anagrams
Problem Statement
Given an array of strings, group anagrams together.
For example, given: ["eat", "tea", "tan", "ate", "nat", "bat"]
,
Return:
[
["ate", "eat","tea"],
["nat","tan"],
["bat"]
]
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Note: All inputs will be in lower-case.
题解1 - 双重for
循环(TLE)
题 Two Strings Are Anagrams 的升级版,容易想到的方法为使用双重for
循环两两判断字符串数组是否互为变位字符串。但显然此法的时间复杂度较高。还需要 的数组来记录字符串是否被加入到最终结果中。
Python
class Solution:
# @param strs: A list of strings
# @return: A list of strings
# @return: A list of strings
def anagrams(self, strs):
if len(strs) < 2 :
return strs
result=[]
visited=[False]*len(strs)
for index1,s1 in enumerate(strs):
hasAnagrams = False
for index2,s2 in enumerate(strs):
if index2 > index1 and not visited[index2] and self.isAnagrams(s1,s2):
result.append(s2)
visited[index2]=True
hasAnagrams = True
if not visited[index1] and hasAnagrams:
result.append(s1)
return result
def isAnagrams(self, str1, str2):
if sorted (str1) == sorted(str2):
return True
return False
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C++
class Solution {
public:
/**
* @param strs: A list of strings
* @return: A list of strings
*/
vector<string> anagrams(vector<string> &strs) {
if (strs.size() < 2) {
return strs;
}
vector<string> result;
vector<bool> visited(strs.size(), false);
for (int s1 = 0; s1 != strs.size(); ++s1) {
bool has_anagrams = false;
for (int s2 = s1 + 1; s2 < strs.size(); ++s2) {
if ((!visited[s2]) && isAnagrams(strs[s1], strs[s2])) {
result.push_back(strs[s2]);
visited[s2] = true;
has_anagrams = true;
}
}
if ((!visited[s1]) && has_anagrams) result.push_back(strs[s1]);
}
return result;
}
private:
bool isAnagrams(string &s, string &t) {
if (s.size() != t.size()) {
return false;
}
const int AlphabetNum = 26;
int letterCount[AlphabetNum] = {0};
for (int i = 0; i != s.size(); ++i) {
++letterCount[s[i] - 'a'];
--letterCount[t[i] - 'a'];
}
for (int i = 0; i != t.size(); ++i) {
if (letterCount[t[i] - 'a'] < 0) {
return false;
}
}
return true;
}
};
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源码分析
- strs 长度小于等于1时直接返回。
- 使用与 strs 等长的布尔数组表示其中的字符串是否被添加到最终的返回结果中。
- 双重循环遍历字符串数组,注意去重即可。
- 私有方法
isAnagrams
用于判断两个字符串是否互为变位词。
复杂度分析
私有方法isAnagrams
最坏的时间复杂度为 , 其中 为字符串长度。双重for
循环时间复杂度近似为 , 为给定字符串数组数目。总的时间复杂度近似为 . 使用了Vector String "visited",空间复杂度可认为是 .
题解2 - 排序 + hashmap
在题 Two Strings Are Anagrams 中曾介绍过使用排序和 hashmap 两种方法判断变位词。这里我们将这两种方法同时引入!只不过此时的 hashmap 的 key 为字符串,value 为该字符串在 vector 中出现的次数。两次遍历字符串数组,第一次遍历求得排序后的字符串数量,第二次遍历将排序后相同的字符串取出放入最终结果中。
leetcode 上此题的 signature 已经更新,需要将 anagrams 按组输出,稍微麻烦一点点。
Python lintcode
class Solution:
# @param strs: A list of strings
# @return: A list of strings
# @return: A list of strings
def anagrams(self, strs):
strDict={}
result=[]
for string in strs:
if "".join(sorted(string)) not in strDict.keys():
strDict["".join(sorted(string))] = 1
else:
strDict["".join(sorted(string))] += 1
for string in strs:
if strDict["".join(sorted(string))] >1:
result.append(string)
return result
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C++ - lintcode
class Solution {
public:
/**
* @param strs: A list of strings
* @return: A list of strings
*/
vector<string> anagrams(vector<string> &strs) {
unordered_map<string, int> hash;
for (int i = 0; i < strs.size(); i++) {
string str = strs[i];
sort(str.begin(), str.end());
++hash[str];
}
vector<string> result;
for (int i = 0; i < strs.size(); i++) {
string str = strs[i];
sort(str.begin(), str.end());
if (hash[str] > 1) {
result.push_back(strs[i]);
}
}
return result;
}
};
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Java - leetcode
public class Solution {
public List<List<String>> groupAnagrams(String[] strs) {
List<List<String>> result = new ArrayList<List<String>>();
if (strs == null) return result;
// one key to multiple value multiMap
Map<String, ArrayList<String>> multiMap = new HashMap<String, ArrayList<String>>();
for (String str : strs) {
char[] strChar = str.toCharArray();
Arrays.sort(strChar);
String strSorted = String.valueOf(strChar);
if (multiMap.containsKey(strSorted)) {
ArrayList<String> aList = multiMap.get(strSorted);
aList.add(str);
multiMap.put(strSorted, aList);
} else {
ArrayList<String> aList = new ArrayList<String>();
aList.add(str);
multiMap.put(strSorted, aList);
}
}
// add List group to result
Set<String> keySet = multiMap.keySet();
for (String key : keySet) {
ArrayList<String> aList = multiMap.get(key);
Collections.sort(aList);
result.add(aList);
}
return result;
}
}
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源码分析
建立 key 为字符串,value 为相应计数器的hashmap, unordered_map
为 C++ 11中引入的哈希表数据结构[^unordered_map], 这种新的数据结构和之前的 map 有所区别,详见[^map-unordered_map]。
第一次遍历字符串数组获得排序后的字符串计数器信息,第二次遍历字符串数组将哈希表中计数器值大于1的字符串取出。
leetcode 中题目 signature 已经有所变化,这里使用一对多的 HashMap 较为合适,使用 ArrayList 作为 value. Java 中对 String 排序可先将其转换为 char[], 排序后再转换为新的 String.
复杂度分析
遍历一次字符串数组,复杂度为 , 对单个字符串排序复杂度近似为 . 两次遍历字符串数组,故总的时间复杂度近似为 . 使用了哈希表,空间复杂度为 , 其中 K 为排序后不同的字符串个数。
Reference
- [^unordered_map]: unordered_map - C++ Reference
- [^map-unordered_map]: c++ - Choosing between std::map and std::unordered_map - Stack Overflow
- Anagrams | 九章算法