- The DSA Woodshed
- Algorithms
- Graphs
- Word Ladder
Word Ladder
Problem
Given two words and a dictionary, find the length of the shortest transformation sequence from beginWord to endWord, such that only one letter can be changed at a time and each intermediate word must exist in the word list.
Approach
BFS level-by-level. At each level, for every word generate all possible one-character mutations and check if they exist in the remaining word set.
When to Use
BFS for shortest transformation sequence — "minimum edits", "fewest steps to transform X into Y", unweighted shortest path in an implicit graph. Keywords: "word ladder", "gene mutation", "lock combination".
Complexity
| Time | `O(n * m^2) where n = |
| Space | O(n * m) |
Source
"""Shortest transformation sequence from beginWord to endWord.
Problem:
Given two words and a dictionary, find the length of the shortest
transformation sequence from beginWord to endWord, such that only
one letter can be changed at a time and each intermediate word
must exist in the word list.
Approach:
BFS level-by-level. At each level, for every word generate all
possible one-character mutations and check if they exist in the
remaining word set.
When to use:
BFS for shortest transformation sequence — "minimum edits", "fewest
steps to transform X into Y", unweighted shortest path in an implicit
graph. Keywords: "word ladder", "gene mutation", "lock combination".
Complexity:
Time: O(n * m^2) where n = |word_list|, m = word length
(m patterns per word, each pattern is O(m) to build)
Space: O(n * m)
"""
from collections import deque
def ladder_length(begin_word: str, end_word: str, word_list: list[str]) -> int:
"""Return the number of words in the shortest transformation sequence.
Returns 0 if no such sequence exists. The count includes both
begin_word and end_word.
>>> ladder_length("hit", "cog", ["hot", "dot", "dog", "lot", "log", "cog"])
5
"""
word_set = set(word_list)
if end_word not in word_set:
return 0
queue: deque[str] = deque([begin_word])
visited: set[str] = {begin_word}
level = 1
while queue:
for _ in range(len(queue)):
word = queue.popleft()
if word == end_word:
return level
for neighbor in _neighbors(word, word_set, visited):
visited.add(neighbor)
queue.append(neighbor)
level += 1
return 0
def _neighbors(
word: str,
word_set: set[str],
visited: set[str],
) -> list[str]:
"""Generate valid one-edit neighbors of *word*."""
result: list[str] = []
word_arr = list(word)
for i in range(len(word_arr)):
original = word_arr[i]
for c in "abcdefghijklmnopqrstuvwxyz":
if c == original:
continue
word_arr[i] = c
candidate = "".join(word_arr)
if candidate in word_set and candidate not in visited:
result.append(candidate)
word_arr[i] = original
return resultThis page lives in git. Anyone can propose an edit. Edit this page View source