最近我一直在iPhone上玩一款名为《Scramble》的游戏。有些人可能知道这个游戏叫拼字游戏。从本质上讲，当游戏开始时，你会得到一个字母矩阵:
F X I E
A M L O
E W B X
A S T U
The goal of the game is to find as many words as you can that can be formed by chaining letters together. You can start with any letter, and all the letters that surround it are fair game, and then once you move on to the next letter, all the letters that surround that letter are fair game, except for any previously used letters. So in the grid above, for example, I could come up with the words LOB, TUX, SEA, FAME, etc. Words must be at least 3 characters, and no more than NxN characters, which would be 16 in this game but can vary in some implementations. While this game is fun and addictive, I am apparently not very good at it and I wanted to cheat a little bit by making a program that would give me the best possible words (the longer the word the more points you get).
(来源:boggled.org)
不幸的是，我不太擅长算法或它们的效率等等。我的第一次尝试使用一个像这样的字典(约2.3MB)，并进行线性搜索，试图匹配字典条目的组合。这需要花费很长时间来找到可能的单词，因为你每轮只有2分钟的时间，这是不够的。
我很有兴趣看看是否有任何Stackoverflowers可以提出更有效的解决方案。我主要是在寻找使用三大p的解决方案:Python、PHP和Perl，尽管任何使用Java或c++的东西也很酷，因为速度是至关重要的。
目前的解决方案:
Adam Rosenfield, Python， ~20岁
John Fouhy, Python， ~3秒
Kent Fredric, Perl， ~1s
Darius Bacon, Python， ~1s
rvarcher, VB。净,~ 1 s
Paolo Bergantino, PHP(实时链接)，~5s(本地~2s)
package ProblemSolving;
import java.util.HashSet;
import java.util.Set;
/**
* Given a 2-dimensional array of characters and a
* dictionary in which a word can be searched in O(1) time.
* Need to print all the words from array which are present
* in dictionary. Word can be formed in any direction but
* has to end at any edge of array.
* (Need not worry much about the dictionary)
*/
public class DictionaryWord {
private static char[][] matrix = new char[][]{
{'a', 'f', 'h', 'u', 'n'},
{'e', 't', 'a', 'i', 'r'},
{'a', 'e', 'g', 'g', 'o'},
{'t', 'r', 'm', 'l', 'p'}
};
private static int dim_x = matrix.length;
private static int dim_y = matrix[matrix.length -1].length;
private static Set<String> wordSet = new HashSet<String>();
public static void main(String[] args) {
//dictionary
wordSet.add("after");
wordSet.add("hate");
wordSet.add("hair");
wordSet.add("air");
wordSet.add("eat");
wordSet.add("tea");
for (int x = 0; x < dim_x; x++) {
for (int y = 0; y < dim_y; y++) {
checkAndPrint(matrix[x][y] + "");
int[][] visitedMap = new int[dim_x][dim_y];
visitedMap[x][y] = 1;
recursion(matrix[x][y] + "", visitedMap, x, y);
}
}
}
private static void checkAndPrint(String word) {
if (wordSet.contains(word)) {
System.out.println(word);
}
}
private static void recursion(String word, int[][] visitedMap, int x, int y) {
for (int i = Math.max(x - 1, 0); i < Math.min(x + 2, dim_x); i++) {
for (int j = Math.max(y - 1, 0); j < Math.min(y + 2, dim_y); j++) {
if (visitedMap[i][j] == 1) {
continue;
} else {
int[][] newVisitedMap = new int[dim_x][dim_y];
for (int p = 0; p < dim_x; p++) {
for (int q = 0; q < dim_y; q++) {
newVisitedMap[p][q] = visitedMap[p][q];
}
}
newVisitedMap[i][j] = 1;
checkAndPrint(word + matrix[i][j]);
recursion(word + matrix[i][j], newVisitedMap, i, j);
}
}
}
}
}
我意识到这个问题的时间来了又去了，但由于我自己正在研究一个求解器，并在谷歌搜索时偶然发现了这个，我想我应该发布一个参考，因为它似乎与其他一些问题有点不同。
我选择在游戏棋盘上使用平面数组，并从棋盘上的每个字母进行递归搜索，从有效邻居遍历到有效邻居，如果索引中的有效前缀是当前字母列表，则扩展搜索。而遍历当前单词的概念是进入板的索引列表，而不是组成单词的字母。在检查索引时，将索引转换为字母并完成检查。
索引是一个蛮力字典，有点像trie，但允许对索引进行python查询。如果单词'cat'和'cater'在列表中，你会在字典中看到:
d = { 'c': ['cat','cater'],
'ca': ['cat','cater'],
'cat': ['cat','cater'],
'cate': ['cater'],
'cater': ['cater'],
}
因此，如果current_word是'ca'，您就知道它是一个有效的前缀，因为'ca'在d中返回True(因此继续遍历板)。如果current_word是'cat'，那么你知道它是一个有效的单词，因为它是一个有效的前缀，并且d['cat']中的'cat'也返回True。
如果感觉这允许一些可读的代码，似乎不是太慢。像其他人一样，这个系统的费用是读取/构建索引。解这个板子相当麻烦。
代码在http://gist.github.com/268079。它是故意垂直和幼稚的，有很多明确的有效性检查，因为我想理解问题，而不是用一堆魔法或晦涩难懂的东西把它弄得乱七八糟。
我的答案和这里的其他答案一样，但我把它贴出来是因为它看起来比其他Python解决方案快一些，因为设置字典更快。(我对比了John Fouhy的解决方案。)设置后，解决的时间在噪声中下降。
grid = "fxie amlo ewbx astu".split()
nrows, ncols = len(grid), len(grid[0])
# A dictionary word that could be a solution must use only the grid's
# letters and have length >= 3. (With a case-insensitive match.)
import re
alphabet = ''.join(set(''.join(grid)))
bogglable = re.compile('[' + alphabet + ']{3,}$', re.I).match
words = set(word.rstrip('\n') for word in open('words') if bogglable(word))
prefixes = set(word[:i] for word in words
for i in range(2, len(word)+1))
def solve():
for y, row in enumerate(grid):
for x, letter in enumerate(row):
for result in extending(letter, ((x, y),)):
yield result
def extending(prefix, path):
if prefix in words:
yield (prefix, path)
for (nx, ny) in neighbors(path[-1]):
if (nx, ny) not in path:
prefix1 = prefix + grid[ny][nx]
if prefix1 in prefixes:
for result in extending(prefix1, path + ((nx, ny),)):
yield result
def neighbors((x, y)):
for nx in range(max(0, x-1), min(x+2, ncols)):
for ny in range(max(0, y-1), min(y+2, nrows)):
yield (nx, ny)
示例用法:
# Print a maximal-length word and its path:
print max(solve(), key=lambda (word, path): len(word))
编辑:过滤掉长度小于3个字母的单词。
编辑2:我很好奇为什么Kent Fredric的Perl解决方案更快;它使用正则表达式匹配，而不是一组字符。在Python中做同样的事情，速度大约会翻倍。
下面是使用NLTK工具包中的预定义单词的解决方案
NLTK有NLTK。语料库包，我们有一个叫做单词的包，它包含超过20万个英语单词，你可以简单地把它们都用到你的程序中。
一旦创建你的矩阵转换成一个字符数组，并执行这段代码
import nltk
from nltk.corpus import words
from collections import Counter
def possibleWords(input, charSet):
for word in input:
dict = Counter(word)
flag = 1
for key in dict.keys():
if key not in charSet:
flag = 0
if flag == 1 and len(word)>5: #its depends if you want only length more than 5 use this otherwise remove that one.
print(word)
nltk.download('words')
word_list = words.words()
# prints 236736
print(len(word_list))
charSet = ['h', 'e', 'l', 'o', 'n', 'v', 't']
possibleWords(word_list, charSet)
输出:
eleven
eleventh
elevon
entente
entone
ethene
ethenol
evolve
evolvent
hellhole
helvell
hooven
letten
looten
nettle
nonene
nonent
nonlevel
notelet
novelet
novelette
novene
teenet
teethe
teevee
telethon
tellee
tenent
tentlet
theelol
toetoe
tonlet
toothlet
tootle
tottle
vellon
velvet
velveteen
venene
vennel
venthole
voeten
volent
volvelle
volvent
voteen
我希望你能得到它。
给定一个有N行M列的Boggle板，让我们假设如下:
N*M基本上大于可能单词的数量
N*M基本上大于可能的最长单词
在这些假设下，该解的复杂度为O(N*M)。
我认为比较这个示例板的运行时间在很多方面都没有重点，但是为了完整性，在我的现代MacBook Pro上，这个解决方案在0.2秒内完成。
这个解决方案将为语料库中的每个单词找到所有可能的路径。
#!/usr/bin/env ruby
# Example usage: ./boggle-solver --board "fxie amlo ewbx astu"
autoload :Matrix, 'matrix'
autoload :OptionParser, 'optparse'
DEFAULT_CORPUS_PATH = '/usr/share/dict/words'.freeze
# Functions
def filter_corpus(matrix, corpus, min_word_length)
board_char_counts = Hash.new(0)
matrix.each { |c| board_char_counts[c] += 1 }
max_word_length = matrix.row_count * matrix.column_count
boggleable_regex = /^[#{board_char_counts.keys.reduce(:+)}]{#{min_word_length},#{max_word_length}}$/
corpus.select{ |w| w.match boggleable_regex }.select do |w|
word_char_counts = Hash.new(0)
w.each_char { |c| word_char_counts[c] += 1 }
word_char_counts.all? { |c, count| board_char_counts[c] >= count }
end
end
def neighbors(point, matrix)
i, j = point
([i-1, 0].max .. [i+1, matrix.row_count-1].min).inject([]) do |r, new_i|
([j-1, 0].max .. [j+1, matrix.column_count-1].min).inject(r) do |r, new_j|
neighbor = [new_i, new_j]
neighbor.eql?(point) ? r : r << neighbor
end
end
end
def expand_path(path, word, matrix)
return [path] if path.length == word.length
next_char = word[path.length]
viable_neighbors = neighbors(path[-1], matrix).select do |point|
!path.include?(point) && matrix.element(*point).eql?(next_char)
end
viable_neighbors.inject([]) do |result, point|
result + expand_path(path.dup << point, word, matrix)
end
end
def find_paths(word, matrix)
result = []
matrix.each_with_index do |c, i, j|
result += expand_path([[i, j]], word, matrix) if c.eql?(word[0])
end
result
end
def solve(matrix, corpus, min_word_length: 3)
boggleable_corpus = filter_corpus(matrix, corpus, min_word_length)
boggleable_corpus.inject({}) do |result, w|
paths = find_paths(w, matrix)
result[w] = paths unless paths.empty?
result
end
end
# Script
options = { corpus_path: DEFAULT_CORPUS_PATH }
option_parser = OptionParser.new do |opts|
opts.banner = 'Usage: boggle-solver --board <value> [--corpus <value>]'
opts.on('--board BOARD', String, 'The board (e.g. "fxi aml ewb ast")') do |b|
options[:board] = b
end
opts.on('--corpus CORPUS_PATH', String, 'Corpus file path') do |c|
options[:corpus_path] = c
end
opts.on_tail('-h', '--help', 'Shows usage') do
STDOUT.puts opts
exit
end
end
option_parser.parse!
unless options[:board]
STDERR.puts option_parser
exit false
end
unless File.file? options[:corpus_path]
STDERR.puts "No corpus exists - #{options[:corpus_path]}"
exit false
end
rows = options[:board].downcase.scan(/\S+/).map{ |row| row.scan(/./) }
raw_corpus = File.readlines(options[:corpus_path])
corpus = raw_corpus.map{ |w| w.downcase.rstrip }.uniq.sort
solution = solve(Matrix.rows(rows), corpus)
solution.each_pair do |w, paths|
STDOUT.puts w
paths.each do |path|
STDOUT.puts "\t" + path.map{ |point| point.inspect }.join(', ')
end
end
STDOUT.puts "TOTAL: #{solution.count}"
