如何找到从字符数组一个字？ [英] How to find a word from arrays of characters?
问题描述
什么是解决这个问题的最好方法：
What is the best way to solve this:
我有一组阵列具有3-4个字符内的每个像这样：
I have a group of arrays with 3-4 characters inside each like so:
{p, {a, {t, {m,
q, b, u, n,
r, c v o
s } } }
}
我也有字典单词的数组。
I also have an array of dictionary words.
什么是发现，如果字符数组可以结合形成的字典单词的一个最好的/最快的方法是什么？例如，上述阵列可以使字：
拍拍，鼠，在，来，流浪汉（笑）
但不是要点或垫子
我应该遍历字典中，如果单词看能够制造或者获取从字母的所有组合再比较这些到字典中
What is the best/fastest way to find if the array of characters can combine to form one of the dictionary words? For example, the above arrays could make the words:
"pat","rat","at","to","bum"(lol)
but not "nub" or "mat"
Should i loop through the dictionary to see if words can be made or get all the combinations from the letters then compare those to the dictionary
推荐答案
我有一些拼字code周围铺设，所以我能一起抛出此。我使用的字典是sowpods（267751字）。下面的code读字典为每行一个大写单词的文本文件。
I had some Scrabble code laying around, so I was able to throw this together. The dictionary I used is sowpods (267751 words). The code below reads the dictionary as a text file with one uppercase word on each line.
在code是C＃：
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.IO;
using System.Diagnostics;
namespace SO_6022848
{
public struct Letter
{
public const string Chars = "ABCDEFGHIJKLMNOPQRSTUVWXYZ";
public static implicit operator Letter(char c)
{
return new Letter() { Index = Chars.IndexOf(c) };
}
public int Index;
public char ToChar()
{
return Chars[Index];
}
public override string ToString()
{
return Chars[Index].ToString();
}
}
public class Trie
{
public class Node
{
public string Word;
public bool IsTerminal { get { return Word != null; } }
public Dictionary<Letter, Node> Edges = new Dictionary<Letter, Node>();
}
public Node Root = new Node();
public Trie(string[] words)
{
for (int w = 0; w < words.Length; w++)
{
var word = words[w];
var node = Root;
for (int len = 1; len <= word.Length; len++)
{
var letter = word[len - 1];
Node next;
if (!node.Edges.TryGetValue(letter, out next))
{
next = new Node();
if (len == word.Length)
{
next.Word = word;
}
node.Edges.Add(letter, next);
}
node = next;
}
}
}
}
class Program
{
static void GenWords(Trie.Node n, HashSet<Letter>[] sets, int currentArrayIndex, List<string> wordsFound)
{
if (currentArrayIndex < sets.Length)
{
foreach (var edge in n.Edges)
{
if (sets[currentArrayIndex].Contains(edge.Key))
{
if (edge.Value.IsTerminal)
{
wordsFound.Add(edge.Value.Word);
}
GenWords(edge.Value, sets, currentArrayIndex + 1, wordsFound);
}
}
}
}
static void Main(string[] args)
{
const int minArraySize = 3;
const int maxArraySize = 4;
const int setCount = 10;
const bool generateRandomInput = true;
var trie = new Trie(File.ReadAllLines("sowpods.txt"));
var watch = new Stopwatch();
var trials = 10000;
var wordCountSum = 0;
var rand = new Random(37);
for (int t = 0; t < trials; t++)
{
HashSet<Letter>[] sets;
if (generateRandomInput)
{
sets = new HashSet<Letter>[setCount];
for (int i = 0; i < setCount; i++)
{
sets[i] = new HashSet<Letter>();
var size = minArraySize + rand.Next(maxArraySize - minArraySize + 1);
while (sets[i].Count < size)
{
sets[i].Add(Letter.Chars[rand.Next(Letter.Chars.Length)]);
}
}
}
else
{
sets = new HashSet<Letter>[] {
new HashSet<Letter>(new Letter[] { 'P', 'Q', 'R', 'S' }),
new HashSet<Letter>(new Letter[] { 'A', 'B', 'C' }),
new HashSet<Letter>(new Letter[] { 'T', 'U', 'V' }),
new HashSet<Letter>(new Letter[] { 'M', 'N', 'O' }) };
}
watch.Start();
var wordsFound = new List<string>();
for (int i = 0; i < sets.Length - 1; i++)
{
GenWords(trie.Root, sets, i, wordsFound);
}
watch.Stop();
wordCountSum += wordsFound.Count;
if (!generateRandomInput && t == 0)
{
foreach (var word in wordsFound)
{
Console.WriteLine(word);
}
}
}
Console.WriteLine("Elapsed per trial = {0}", new TimeSpan(watch.Elapsed.Ticks / trials));
Console.WriteLine("Average word count per trial = {0:0.0}", (float)wordCountSum / trials);
}
}
}
下面使用的测试数据时是输出：
Here is the output when using your test data:
PA
PAT
PAV
QAT
RAT
RATO
RAUN
SAT
SAU
SAV
SCUM
AT
AVO
BUM
BUN
CUM
TO
UM
UN
Elapsed per trial = 00:00:00.0000725
Average word count per trial = 19.0
和输出使用随机数据时（不打印每一个字）：
And the output when using random data (does not print each word):
Elapsed per trial = 00:00:00.0002910
Average word count per trial = 62.2
编辑：我做了两个变化要快得多：存放字的线索的每个终端节点，使其不必进行重建。并存储所述输入的字母作为散列的阵列设置数组的数组代替，以使包含（）调用快
I made it much faster with two changes: Storing the word at each terminal node of the trie, so that it doesn't have to be rebuilt. And storing the input letters as an array of hash sets instead of an array of arrays, so that the Contains() call is fast.
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