- Preface
- FAQ
- Guidelines for Contributing
- Contributors
- Part I - Basics
- Basics Data Structure
- String
- Linked List
- Binary Tree
- Huffman Compression
- Queue
- Heap
- Stack
- Set
- Map
- Graph
- Basics Sorting
- 算法复习——排序
- Bubble Sort
- Selection Sort
- Insertion Sort
- Merge Sort
- Quick Sort
- Heap Sort
- Bucket Sort
- Counting Sort
- Radix Sort
- Basics Algorithm
- Divide and Conquer
- Binary Search
- Math
- Greatest Common Divisor
- Prime
- Knapsack
- Probability
- Shuffle
- Bitmap
- Basics Misc
- Bit Manipulation
- Part II - Coding
- String
- strStr
- Two Strings Are Anagrams
- Compare Strings
- Anagrams
- Longest Common Substring
- Rotate String
- Reverse Words in a String
- Valid Palindrome
- Longest Palindromic Substring
- Space Replacement
- Wildcard Matching
- Length of Last Word
- Count and Say
- Integer Array
- Remove Element
- Zero Sum Subarray
- Subarray Sum K
- Subarray Sum Closest
- Recover Rotated Sorted Array
- Product of Array Exclude Itself
- Partition Array
- First Missing Positive
- 2 Sum
- 3 Sum
- 3 Sum Closest
- Remove Duplicates from Sorted Array
- Remove Duplicates from Sorted Array II
- Merge Sorted Array
- Merge Sorted Array II
- Median
- Partition Array by Odd and Even
- Kth Largest Element
- Binary Search
- Binary Search
- Search Insert Position
- Search for a Range
- First Bad Version
- Search a 2D Matrix
- Search a 2D Matrix II
- Find Peak Element
- Search in Rotated Sorted Array
- Search in Rotated Sorted Array II
- Find Minimum in Rotated Sorted Array
- Find Minimum in Rotated Sorted Array II
- Median of two Sorted Arrays
- Sqrt x
- Wood Cut
- Math and Bit Manipulation
- Single Number
- Single Number II
- Single Number III
- O1 Check Power of 2
- Convert Integer A to Integer B
- Factorial Trailing Zeroes
- Unique Binary Search Trees
- Update Bits
- Fast Power
- Hash Function
- Count 1 in Binary
- Fibonacci
- A plus B Problem
- Print Numbers by Recursion
- Majority Number
- Majority Number II
- Majority Number III
- Digit Counts
- Ugly Number
- Plus One
- Linked List
- Remove Duplicates from Sorted List
- Remove Duplicates from Sorted List II
- Remove Duplicates from Unsorted List
- Partition List
- Add Two Numbers
- Two Lists Sum Advanced
- Remove Nth Node From End of List
- Linked List Cycle
- Linked List Cycle II
- Reverse Linked List
- Reverse Linked List II
- Merge Two Sorted Lists
- Merge k Sorted Lists
- Reorder List
- Copy List with Random Pointer
- Sort List
- Insertion Sort List
- Palindrome Linked List
- Delete Node in the Middle of Singly Linked List
- Rotate List
- Swap Nodes in Pairs
- Remove Linked List Elements
- Binary Tree
- Binary Tree Preorder Traversal
- Binary Tree Inorder Traversal
- Binary Tree Postorder Traversal
- Binary Tree Level Order Traversal
- Binary Tree Level Order Traversal II
- Maximum Depth of Binary Tree
- Balanced Binary Tree
- Binary Tree Maximum Path Sum
- Lowest Common Ancestor
- Invert Binary Tree
- Diameter of a Binary Tree
- Construct Binary Tree from Preorder and Inorder Traversal
- Construct Binary Tree from Inorder and Postorder Traversal
- Subtree
- Binary Tree Zigzag Level Order Traversal
- Binary Tree Serialization
- Binary Search Tree
- Insert Node in a Binary Search Tree
- Validate Binary Search Tree
- Search Range in Binary Search Tree
- Convert Sorted Array to Binary Search Tree
- Convert Sorted List to Binary Search Tree
- Binary Search Tree Iterator
- Exhaustive Search
- Subsets
- Unique Subsets
- Permutations
- Unique Permutations
- Next Permutation
- Previous Permuation
- Permutation Index
- Permutation Index II
- Permutation Sequence
- Unique Binary Search Trees II
- Palindrome Partitioning
- Combinations
- Combination Sum
- Combination Sum II
- Minimum Depth of Binary Tree
- Word Search
- Dynamic Programming
- Triangle
- Backpack
- Backpack II
- Minimum Path Sum
- Unique Paths
- Unique Paths II
- Climbing Stairs
- Jump Game
- Word Break
- Longest Increasing Subsequence
- Follow up
- Palindrome Partitioning II
- Longest Common Subsequence
- Edit Distance
- Jump Game II
- Best Time to Buy and Sell Stock
- Best Time to Buy and Sell Stock II
- Best Time to Buy and Sell Stock III
- Best Time to Buy and Sell Stock IV
- Distinct Subsequences
- Interleaving String
- Maximum Subarray
- Maximum Subarray II
- Longest Increasing Continuous subsequence
- Longest Increasing Continuous subsequence II
- Maximal Square
- Graph
- Find the Connected Component in the Undirected Graph
- Route Between Two Nodes in Graph
- Topological Sorting
- Word Ladder
- Bipartial Graph Part I
- Data Structure
- Implement Queue by Two Stacks
- Min Stack
- Sliding Window Maximum
- Longest Words
- Heapify
- Problem Misc
- Nuts and Bolts Problem
- String to Integer
- Insert Interval
- Merge Intervals
- Minimum Subarray
- Matrix Zigzag Traversal
- Valid Sudoku
- Add Binary
- Reverse Integer
- Gray Code
- Find the Missing Number
- Minimum Window Substring
- Continuous Subarray Sum
- Continuous Subarray Sum II
- Longest Consecutive Sequence
- Part III - Contest
- Google APAC
- APAC 2015 Round B
- Problem A. Password Attacker
- APAC 2016 Round D
- Problem A. Dynamic Grid
- Microsoft
- Microsoft 2015 April
- Problem A. Magic Box
- Problem B. Professor Q's Software
- Problem C. Islands Travel
- Problem D. Recruitment
- Microsoft 2015 April 2
- Problem A. Lucky Substrings
- Problem B. Numeric Keypad
- Problem C. Spring Outing
- Microsoft 2015 September 2
- Problem A. Farthest Point
- Appendix I Interview and Resume
- Interview
- Resume
- 術語表
strStr
Source
- leetcode: Implement strStr() | LeetCode OJ
- lintcode: lintcode - (13) strstr
Problem
For a given source string and a target string, you should output the first index(from 0) of target string in source string.
If target does not exist in source, just return -1
.
Example
If source = "source"
and target = "target"
, return -1
.
If source = "abcdabcdefg"
and target = "bcd"
, return 1
.
Challenge
O(n2) is acceptable. Can you implement an O(n) algorithm? (hint: KMP)
Clarification
Do I need to implement KMP Algorithm in a real interview?
- Not necessary. When you meet this problem in a real interview, the interviewer may just want to test your basic implementation ability. But make sure your confirm with the interviewer first.
题解
对于字符串查找问题,可使用双重 for 循环解决,效率更高的则为 KMP 算法。
Python
class Solution:
def strStr(self, source, target):
if source is None or target is None:
return -1
for i in xrange(len(source) - len(target) + 1):
for j in xrange(len(target)):
if source[i + j] != target[j]:
break
else: # no break
return i
return -1
C++
class Solution {
public:
int strStr(string haystack, string needle) {
if (haystack.empty() && needle.empty()) return 0;
if (haystack.empty()) return -1;
if (needle.empty()) return 0;
if (haystack.size() < needle.size()) return -1;
for (int i = 0; i < haystack.size() - needle.size() + 1; i++) {
int j = 0;
for (; j < needle.size(); j++) {
if (haystack[i + j] != needle[j]) break;
}
if (j == needle.size()) return i;
}
return -1;
}
};
Java
/**
* http://www.jiuzhang.com//solutions/implement-strstr
*/
class Solution {
/**
* Returns a index to the first occurrence of target in source,
* or -1 if target is not part of source.
* @param source string to be scanned.
* @param target string containing the sequence of characters to match.
*/
public int strStr(String source, String target) {
if (source == null || target == null) {
return -1;
}
int i, j;
for (i = 0; i < source.length() - target.length() + 1; i++) {
for (j = 0; j < target.length(); j++) {
if (source.charAt(i + j) != target.charAt(j)) {
break;
} //if
} //for j
if (j == target.length()) {
return i;
}
} //for i
// did not find the target
return -1;
}
}
源码分析
- 边界检查:
source
和target
有可能是空串。 - 边界检查之下标溢出:注意变量
i
的循环判断条件,如果是单纯的i < source.length()
则在后面的source.charAt(i + j)
时有可能溢出。 - 代码风格:
- 运算符
==
两边应加空格 - 变量名不要起
s1``s2
这类,要有意义,如target``source
- 即使 if 语句中只有一句话也要加大括号,即
{return -1;}
- Java 代码的大括号一般在同一行右边,C++ 代码的大括号一般另起一行
- int i, j;`声明前有一行空格,是好的代码风格。
- 运算符
- 是否在 for 的条件中声明
i
,j
,这个视情况而定,如果需要在循环外再使用时,则须在外部初始化,否则没有这个必要。
需要注意的是有些题目要求并不是返回索引,而是返回字符串,此时还需要调用相应语言的 substring
方法。
Python 代码中的 else
接的是 for
而不是 if
, 其含义为 no break
, 属于比较 Pythonic 的用法,有兴趣的可以参考 4. More Control Flow Tools 的 4.4 节和 if statement - Why does python use 'else' after for and while loops?
复杂度分析
双重 for 循环,时间复杂度为 O(nm)O(nm)O(nm).
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