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Word Ladder II

描述

Given two words (start and end), and a dictionary, find all shortest transformation sequence(s) from start to end, such that:

  • Only one letter can be changed at a time
  • Each intermediate word must exist in the dictionary

For example, Given:

start = "hit"
end = "cog"
dict = ["hot","dot","dog","lot","log"]

Return

[
["hit","hot","dot","dog","cog"],
["hit","hot","lot","log","cog"]
]

Note:

  • All words have the same length.
  • All words contain only lowercase alphabetic characters.

分析

跟 Word Ladder 比,这题是求路径本身,不是路径长度,也是 BFS,略微麻烦点。

求一条路径和求所有路径有很大的不同,求一条路径,每个状态节点只需要记录一个前驱即可;求所有路径时,有的状态节点可能有多个父节点,即要记录多个前驱。

如果当前路径长度已经超过当前最短路径长度,可以中止对该路径的处理,因为我们要找的是最短路径。

单队列

// Word Ladder II
// 时间复杂度O(n),空间复杂度O(n)
public class Solution {
public List<List<String>> findLadders(String beginWord, String endWord,
Set<String> wordList) {
Queue<String> q = new LinkedList<>();
HashMap<String, Integer> visited = new HashMap<>(); // 判重
HashMap<String, ArrayList<String>> father = new HashMap<>(); // DAG

final Function<String, Boolean> stateIsValid = (String s) ->
wordList.contains(s) || s.equals(endWord);
final Function<String, Boolean> stateIsTarget = (String s) ->
s.equals(endWord);

final Function<String, HashSet<String> > stateExtend = (String s) -> {
HashSet<String> result = new HashSet<>();

char[] array = s.toCharArray();
for (int i = 0; i < array.length; ++i) {
final char old = array[i];
for (char c = 'a'; c <= 'z'; c++) {
// 防止同字母替换
if (c == array[i]) continue;

array[i] = c;
String newState = new String(array);
final int newDepth = visited.get(s) + 1;

if (stateIsValid.apply(newState)) {
if (visited.containsKey(newState)) {
final int depth = visited.get(newState);
if (depth < newDepth) {
// do nothing
} else if (depth == newDepth) {
result.add(newState);
} else {
throw new IllegalStateException("not possible to get here");
}

} else {
result.add(newState);
}
}
array[i] = old; // 恢复该单词
}
}

return result;
};

List<List<String>> result = new ArrayList<>();
q.offer(beginWord);
visited.put(beginWord, 0);
while (!q.isEmpty()) {
String state = q.poll();

// 如果当前路径长度已经超过当前最短路径长度,
// 可以中止对该路径的处理,因为我们要找的是最短路径
if (!result.isEmpty() && (visited.get(state) + 1) > result.get(0).size()) break;

if (stateIsTarget.apply(state)) {
ArrayList<String> path = new ArrayList<>();
genPath(father, beginWord, state, path, result);
continue;
}
// 必须挪到下面,比如同一层A和B两个节点均指向了目标节点,
// 那么目标节点就会在q中出现两次,输出路径就会翻倍
// visited.insert(state);

// 扩展节点
HashSet<String> newStates = stateExtend.apply(state);
for (String newState : newStates) {
if (!visited.containsKey(newState)) {
q.offer(newState);
visited.put(newState, visited.get(state)+1);
}
ArrayList<String> parents = father.getOrDefault(newState, new ArrayList<>());
parents.add(state);
father.put(newState, parents);
}
}
return result;
}
private static void genPath(HashMap<String, ArrayList<String>> father,
String start, String state, List<String> path,
List<List<String>> result) {
path.add(state);
if (state.equals(start)) {
if (!result.isEmpty()) {
if (path.size() < result.get(0).size()) {
result.clear();
} else if (path.size() == result.get(0).size()) {
// do nothing
} else {
throw new IllegalStateException("not possible to get here");
}
}
ArrayList<String> tmp = new ArrayList<>(path);
Collections.reverse(tmp);
result.add(tmp);
} else {
for (String f : father.get(state)) {
genPath(father, start, f, path, result);
}
}
path.remove(path.size() - 1);
}
}

双队列

// Word Ladder II
// 时间复杂度O(n),空间复杂度O(n)
public class Solution {
public List<List<String>> findLadders(String beginWord, String endWord,
Set<String> wordList) {
// 当前层,下一层,用unordered_set是为了去重,例如两个父节点指向
// 同一个子节点,如果用vector, 子节点就会在next里出现两次,其实此
// 时 father 已经记录了两个父节点,next里重复出现两次是没必要的
HashSet<String> current = new HashSet<>();
HashSet<String> next = new HashSet<>();
HashSet<String> visited = new HashSet<>(); // 判重
HashMap<String, ArrayList<String>> father = new HashMap<>(); // DAG
int level = -1; // 层次

final Function<String, Boolean> stateIsValid = (String s) ->
wordList.contains(s) || s.equals(endWord);
final Function<String, Boolean> stateIsTarget = (String s) ->
s.equals(endWord);

final Function<String, HashSet<String> > stateExtend = (String s) -> {
HashSet<String> result = new HashSet<>();

char[] array = s.toCharArray();
for (int i = 0; i < array.length; ++i) {
final char old = array[i];
for (char c = 'a'; c <= 'z'; c++) {
// 防止同字母替换
if (c == array[i]) continue;

array[i] = c;
String newState = new String(array);

if (stateIsValid.apply(newState) &&
!visited.contains(newState)) {
result.add(newState);
}
array[i] = old; // 恢复该单词
}
}

return result;
};

List<List<String>> result = new ArrayList<>();
current.add(beginWord);
while (!current.isEmpty()) {
++ level;
// 如果当前路径长度已经超过当前最短路径长度,
// 可以中止对该路径的处理,因为我们要找的是最短路径
if (!result.isEmpty() && level + 1 > result.get(0).size()) break;

// 1. 延迟加入visited, 这样才能允许两个父节点指向同一个子节点
// 2. 一股脑current 全部加入visited, 是防止本层前一个节点扩展
// 节点时,指向了本层后面尚未处理的节点,这条路径必然不是最短的
for (String state : current)
visited.add(state);

for (String state : current) {
if (stateIsTarget.apply(state)) {
ArrayList<String> path = new ArrayList<>();
genPath(father, beginWord, state, path, result);
continue;
}
// 扩展节点
HashSet<String> newStates = stateExtend.apply(state);
for (String newState : newStates) {
next.add(newState);
ArrayList<String> parents = father.getOrDefault(newState, new ArrayList<>());
parents.add(state);
father.put(newState, parents);
}
}
current.clear();
// swap
HashSet<String> tmp = current;
current = next;
next = tmp;

}
return result;
}
private static void genPath(HashMap<String, ArrayList<String>> father,
String start, String state, List<String> path,
List<List<String>> result) {
path.add(state);
if (state.equals(start)) {
if (!result.isEmpty()) {
if (path.size() < result.get(0).size()) {
result.clear();
} else if (path.size() == result.get(0).size()) {
// do nothing
} else {
throw new IllegalStateException("not possible to get here");
}
}
ArrayList<String> tmp = new ArrayList<>(path);
Collections.reverse(tmp);
result.add(tmp);
} else {
for (String f : father.get(state)) {
genPath(father, start, f, path, result);
}
}
path.remove(path.size() - 1);
}
}

图的广搜

前面的解法,在状态扩展的时候,每次都是从'a'到'z'全部枚举一遍,重复计算,比较浪费,其实当给定字典dict后,单词与单词之间的路径就固定下来了,本质上单词与单词之间构成了一个无向图。如果事先把这个图构建出来,那么状态扩展就会大大加快。

import java.util.*;
import java.util.function.Predicate;
import java.util.function.Function;

// Word Ladder II
// 时间复杂度O(n),空间复杂度O(n)
public class Solution {
public List<List<String>> findLadders(String beginWord, String endWord,
Set<String> wordList) {
Queue<String> q = new LinkedList<>();
HashMap<String, Integer> visited = new HashMap<>(); // 判重
HashMap<String, ArrayList<String>> father = new HashMap<>(); // DAG
// only used by stateExtend()
final HashMap<String, HashSet<String>> g = buildGraph(wordList);

final Function<String, Boolean> stateIsValid = (String s) ->
wordList.contains(s) || s.equals(endWord);
final Function<String, Boolean> stateIsTarget = (String s) ->
s.equals(endWord);

final Function<String, List<String> > stateExtend = (String s) -> {
List<String> result = new ArrayList<>();
final int newDepth = visited.get(s) + 1;
HashSet<String> list = g.get(s);
if (list == null) return result;

for (String newState : list) {
if (stateIsValid.apply(newState)) {
if (visited.containsKey(newState)) {
final int depth = visited.get(newState);
if (depth < newDepth) {
// do nothing
} else if (depth == newDepth) {
result.add(newState);
} else {
throw new IllegalStateException("not possible to get here");
}

} else {
result.add(newState);
}
}
}

return result;
};

List<List<String>> result = new ArrayList<>();
q.offer(beginWord);
visited.put(beginWord, 0);
while (!q.isEmpty()) {
String state = q.poll();

// 如果当前路径长度已经超过当前最短路径长度,
// 可以中止对该路径的处理,因为我们要找的是最短路径
if (!result.isEmpty() && (visited.get(state) + 1) > result.get(0).size()) break;

if (stateIsTarget.apply(state)) {
ArrayList<String> path = new ArrayList<>();
genPath(father, beginWord, state, path, result);
continue;
}
// 必须挪到下面,比如同一层A和B两个节点均指向了目标节点,
// 那么目标节点就会在q中出现两次,输出路径就会翻倍
// visited.insert(state);

// 扩展节点
List<String> newStates = stateExtend.apply(state);
for (String newState : newStates) {
if (!visited.containsKey(newState)) {
q.offer(newState);
visited.put(newState, visited.get(state)+1);
}
ArrayList<String> parents = father.getOrDefault(newState, new ArrayList<>());
parents.add(state);
father.put(newState, parents);
}
}
return result;
}
private static void genPath(HashMap<String, ArrayList<String>> father,
String start, String state, List<String> path,
List<List<String>> result) {
path.add(state);
if (state.equals(start)) {
if (!result.isEmpty()) {
if (path.size() < result.get(0).size()) {
result.clear();
} else if (path.size() == result.get(0).size()) {
// do nothing
} else {
throw new IllegalStateException("not possible to get here");
}
}
ArrayList<String> tmp = new ArrayList<>(path);
Collections.reverse(tmp);
result.add(tmp);
} else {
for (String f : father.get(state)) {
genPath(father, start, f, path, result);
}
}
path.remove(path.size() - 1);
}

private static HashMap<String, HashSet<String>> buildGraph(Set<String> dict) {
HashMap<String, HashSet<String>> adjacency_list = new HashMap<>();
for (String word: dict) {
char[] array = word.toCharArray();
for (int i = 0; i < array.length; ++i) {
final char old = array[i];
for (char c = 'a'; c <= 'z'; c++) {
// 防止同字母替换
if (c == array[i]) continue;

array[i] = c;
String newWord = new String(array);

if (dict.contains(newWord)) {
HashSet<String> list = adjacency_list.getOrDefault(
word, new HashSet<>());
list.add(newWord);
adjacency_list.put(word, list);
}
array[i] = old; // 恢复该单词
}
}
}
return adjacency_list;
}
}

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