最近在一次工作面试中，我遇到了以下问题：
编写一个能够在命令行上作为python运行的脚本
它应该在命令行中包含两个单词（或者，如果您愿意，它可以通过控制台查询用户以提供这两个单词）。
鉴于这两个词：确保它们的长度相等 B. 确保它们都是您下载的英语有效单词词典中的单词。
如果是这样，计算您是否可以通过以下一系列步骤从第一个单词到达第二个单词 您可以一次更改一个字母 b. 每次更改字母时，结果单词也必须存在于字典中 c。您不能添加或删除字母
如果这两个词是可达的，脚本应该打印出作为从一个词到另一个词的单一最短路径的路径。
您可以 /usr/share/dict/words 为您的单词词典。
我的解决方案包括使用广度优先搜索来找到两个单词之间的最短路径。但显然这还不足以得到这份工作:(
你们知道我做错了什么吗？太感谢了。
import collections
import functools
import re
def time_func(func):
import time
def wrapper(*args, **kwargs):
start = time.time()
res = func(*args, **kwargs)
timed = time.time() - start
setattr(wrapper, 'time_taken', timed)
return res
functools.update_wrapper(wrapper, func)
return wrapper
class OneLetterGame:
def __init__(self, dict_path):
self.dict_path = dict_path
self.words = set()
def run(self, start_word, end_word):
'''Runs the one letter game with the given start and end words.
'''
assert len(start_word) == len(end_word), \
'Start word and end word must of the same length.'
self.read_dict(len(start_word))
path = self.shortest_path(start_word, end_word)
if not path:
print 'There is no path between %s and %s (took %.2f sec.)' % (
start_word, end_word, find_shortest_path.time_taken)
else:
print 'The shortest path (found in %.2f sec.) is:\n=> %s' % (
self.shortest_path.time_taken, ' -- '.join(path))
def _bfs(self, start):
'''Implementation of breadth first search as a generator.
The portion of the graph to explore is given on demand using get_neighboors.
Care was taken so that a vertex / node is explored only once.
'''
queue = collections.deque([(None, start)])
inqueue = set([start])
while queue:
parent, node = queue.popleft()
yield parent, node
new = set(self.get_neighbours(node)) - inqueue
inqueue = inqueue | new
queue.extend([(node, child) for child in new])
@time_func
def shortest_path(self, start, end):
'''Returns the shortest path from start to end using bfs.
'''
assert start in self.words, 'Start word not in dictionnary.'
assert end in self.words, 'End word not in dictionnary.'
paths = {None: []}
for parent, child in self._bfs(start):
paths[child] = paths[parent] + [child]
if child == end:
return paths[child]
return None
def get_neighbours(self, word):
'''Gets every word one letter away from the a given word.
We do not keep these words in memory because bfs accesses
a given vertex only once.
'''
neighbours = []
p_word = ['^' + word[0:i] + '\w' + word[i+1:] + '$'
for i, w in enumerate(word)]
p_word = '|'.join(p_word)
for w in self.words:
if w != word and re.match(p_word, w, re.I|re.U):
neighbours += [w]
return neighbours
def read_dict(self, size):
'''Loads every word of a specific size from the dictionnary into memory.
'''
for l in open(self.dict_path):
l = l.decode('latin-1').strip().lower()
if len(l) == size:
self.words.add(l)
if __name__ == '__main__':
import sys
if len(sys.argv) not in [3, 4]:
print 'Usage: python one_letter_game.py start_word end_word'
else:
g = OneLetterGame(dict_path = '/usr/share/dict/words')
try:
g.run(*sys.argv[1:])
except AssertionError, e:
print e
谢谢你所有的好答案。我认为真正让我着迷的是我每次都会遍历字典中的所有单词以考虑可能的单词邻居。相反，我可以使用 Duncan 和 Matt Anderson 指出的倒排索引。A* 方法肯定也会有所帮助。非常感谢，现在我知道我做错了什么。
这是带有倒排索引的相同代码：
import collections
import functools
import re
def time_func(func):
import time
def wrapper(*args, **kwargs):
start = time.time()
res = func(*args, **kwargs)
timed = time.time() - start
setattr(wrapper, 'time_taken', timed)
return res
functools.update_wrapper(wrapper, func)
return wrapper
class OneLetterGame:
def __init__(self, dict_path):
self.dict_path = dict_path
self.words = {}
def run(self, start_word, end_word):
'''Runs the one letter game with the given start and end words.
'''
assert len(start_word) == len(end_word), \
'Start word and end word must of the same length.'
self.read_dict(len(start_word))
path = self.shortest_path(start_word, end_word)
if not path:
print 'There is no path between %s and %s (took %.2f sec.)' % (
start_word, end_word, self.shortest_path.time_taken)
else:
print 'The shortest path (found in %.2f sec.) is:\n=> %s' % (
self.shortest_path.time_taken, ' -- '.join(path))
def _bfs(self, start):
'''Implementation of breadth first search as a generator.
The portion of the graph to explore is given on demand using get_neighboors.
Care was taken so that a vertex / node is explored only once.
'''
queue = collections.deque([(None, start)])
inqueue = set([start])
while queue:
parent, node = queue.popleft()
yield parent, node
new = set(self.get_neighbours(node)) - inqueue
inqueue = inqueue | new
queue.extend([(node, child) for child in new])
@time_func
def shortest_path(self, start, end):
'''Returns the shortest path from start to end using bfs.
'''
assert self.in_dictionnary(start), 'Start word not in dictionnary.'
assert self.in_dictionnary(end), 'End word not in dictionnary.'
paths = {None: []}
for parent, child in self._bfs(start):
paths[child] = paths[parent] + [child]
if child == end:
return paths[child]
return None
def in_dictionnary(self, word):
for s in self.get_steps(word):
if s in self.words:
return True
return False
def get_neighbours(self, word):
'''Gets every word one letter away from the a given word.
'''
for step in self.get_steps(word):
for neighbour in self.words[step]:
yield neighbour
def get_steps(self, word):
return (word[0:i] + '*' + word[i+1:]
for i, w in enumerate(word))
def read_dict(self, size):
'''Loads every word of a specific size from the dictionnary into an inverted index.
'''
for w in open(self.dict_path):
w = w.decode('latin-1').strip().lower()
if len(w) != size:
continue
for step in self.get_steps(w):
if step not in self.words:
self.words[step] = []
self.words[step].append(w)
if __name__ == '__main__':
import sys
if len(sys.argv) not in [3, 4]:
print 'Usage: python one_letter_game.py start_word end_word'
else:
g = OneLetterGame(dict_path = '/usr/share/dict/words')
try:
g.run(*sys.argv[1:])
except AssertionError, e:
print e
以及时间比较：
% python one_letter_game_old.py happy hello 最短路径（在 91.57 秒内找到）是：
=> happy -- harpy -- harps -- harts -- halts -- halls -- hells -- hello% python one_letter_game.py happy hello 最短路径（在 1.71 秒内找到）是：
=> happy -- harpy -- harps -- harts -- halts -- halls -- hells -- hello