題目

Given two words (beginWord and endWord), and a dictionary's word list, find the length of shortest transformation sequence from beginWord to endWord, such that:

Only one letter can be changed at a time.
Each transformed word must exist in the word list. Note that beginWord is not a transformed word.

For example,

Given:
beginWord = "hit"
endWord = "cog"
wordList = ["hot","dot","dog","lot","log","cog"]

As one shortest transformation is "hit" -> "hot" -> "dot" -> "dog" -> "cog",
return its length 5.

Note:

• Return 0 if there is no such transformation sequence.
• All words have the same length.
• All words contain only lowercase alphabetic characters.
• You may assume no duplicates in the word list.
• You may assume beginWord and endWord are non-empty and are not the same.

解題之法

class Solution {
public:
    int ladderLength(string beginWord, string endWord, unordered_set<string>& wordDict) {
        wordDict.insert(endWord);
        queue<string> toVisit;
        addNextWords(beginWord, wordDict, toVisit);
        int dist = 2;
        while (!toVisit.empty()) {
            int num = toVisit.size();
            for (int i = 0; i < num; i++) {
                string word = toVisit.front();
                toVisit.pop();
                if (word == endWord) return dist;
                addNextWords(word, wordDict, toVisit);
            }
            dist++;
        }
    }
private:
    void addNextWords(string word, unordered_set<string>& wordDict, queue<string>& toVisit) {
        wordDict.erase(word);
        for (int p = 0; p < (int)word.length(); p++) {
            char letter = word[p];
            for (int k = 0; k < 26; k++) { 
                word[p] = 'a' + k;
                if (wordDict.find(word) != wordDict.end()) {
                    toVisit.push(word);
                    wordDict.erase(word);
                }
            }
            word[p] = letter;
        } 
    } 
};

分析

This problem has a nice BFS structure.

Let's see the example in the problem statement.

start = "hit"

end = "cog"

dict = ["hot", "dot", "dog", "lot", "log"]

Since only one letter can be changed at a time, if we start from "hit", we can only change to those words which have only one different letter from it, like "hot". Putting in graph-theoretic terms, we can say that "hot" is a neighbor of "hit".

The idea is simple to begin from start, then visit its neighbors, then the non-visited neighbors of its neighbors... Well, this is just the typical BFS structure.

To simplify the problem, we insert end into dict. Once we meet end during the BFS, we know we have found the answer. We maintain a variable dist for the current distance of the transformation and update it by dist++ after we finish a round of BFS search (note that it should fit the definition of the distance in the problem statement). Also, to avoid visiting a word for more than once, we erase it from dict once it is visited.