问题
Given a set of words, we need to find the anagram words and display each category alone using the best algorithm.
input:
man car kile arc none like
output:
man
car arc
kile like
none
The best solution I am developing now is based on an hashtable, but I am thinking about equation to convert anagram word into integer value.
Example: man => 'm'+'a'+'n' but this will not give unique values.
Any suggestion?
See following code in C#:
string line = Console.ReadLine();
string []words=line.Split(' ');
int[] numbers = GetUniqueInts(words);
for (int i = 0; i < words.Length; i++)
{
if (table.ContainsKey(numbers[i]))
{
table[numbers[i]] = table[numbers[i]].Append(words[i]);
}
else
{
table.Add(numbers[i],new StringBuilder(words[i]));
}
}
The problem is how to develop GetUniqueInts(string []) method.
回答1:
Don't bother with a custom hash function at all. Use the normal string hash function on whatever your platform is. The important thing is to make the key for your hash table the idea of a "sorted word" - where the word is sorted by letter, so "car" => "acr". All anagrams will have the same "sorted word".
Just have a hash from "sorted word" to "list of words for that sorted word". In LINQ this is incredibly easy:
using System;
using System.Collections.Generic;
using System.Linq;
class FindAnagrams
{
static void Main(string[] args)
{
var lookup = args.ToLookup(word => SortLetters(word));
foreach (var entry in lookup)
{
foreach (var word in entry)
{
Console.Write(word);
Console.Write(" ");
}
Console.WriteLine();
}
}
static string SortLetters(string original)
{
char[] letters = original.ToCharArray();
Array.Sort(letters);
return new string(letters);
}
}
Sample use:
c:\Users\Jon\Test>FindAnagrams.exe man car kile arc none like
man
car arc
kile like
none
回答2:
I used a Godel-inspired scheme:
Assign the primes P_1 to P_26 to the letters (in any order, but to obtain smallish hash values best to give common letters small primes).
Built a histogram of the letters in the word.
Then the hash value is the product of each letter's associated prime raised to the power of its frequency. This gives a unique value to every anagram.
Python code:
primes = [2, 41, 37, 47, 3, 67, 71, 23, 5, 101, 61, 17, 19, 13, 31, 43, 97, 29, 11, 7, 73, 83, 79, 89, 59, 53]
def get_frequency_map(word):
map = {}
for letter in word:
map[letter] = map.get(letter, 0) + 1
return map
def hash(word):
map = get_frequency_map(word)
product = 1
for letter in map.iterkeys():
product = product * primes[ord(letter)-97] ** map.get(letter, 0)
return product
This cleverly transforms the tricky problem of finding subanagrams into the (also known to be tricky) problem of factoring large numbers...
回答3:
A Python version for giggles:
from collections import defaultdict
res = defaultdict(list)
L = "car, acr, bat, tab, get, cat".split(", ")
for w in L:
res["".join(sorted(w))].append(w)
print(res.values())
回答4:
I don't think you'll find anything better than a hash table with a custom hash function (that would sort the letters of he word before hashing it).
Sum of the letters will never work, because you can't really make 'ac' and 'bb' different.
回答5:
You will need large integers (or a bit vector actually) but the following might work
the first occurrence of each letter get's assigned the bit number for that letter, the second occurence gets the bit number for that letter + 26.
For example
a #1 = 1 b #1 = 2 c #1 = 4 a #2 = 2^26 b #2 = 2 ^ 27
You can then sum these together, to get a unique value for the word based on it's letters.
Your storage requirements for the word values will be:
n * 26 bits
where n is the maximum number of occurrences of any repeated letter.
回答6:
I wouldn't use hashing since it adds additional complexity for look-up and adds. Hashing, sorting and multiplications are all going to be slower than a simple array-based histogram solution with tracking uniques. Worst case is O(2n):
// structured for clarity
static bool isAnagram(String s1, String s2)
{
int[] histogram = new int[256];
int uniques = 0;
// scan first string
foreach (int c in s1)
{
// count occurrence
int count = ++histogram[c];
// count uniques
if (count == 1)
{
++uniques;
}
}
// scan second string
foreach (int c in s2)
{
// reverse count occurrence
int count = --histogram[c];
// reverse count uniques
if (count == 0)
{
--uniques;
}
else if (count < 0) // trivial reject of longer strings or more occurrences
{
return false;
}
}
// final histogram unique count should be 0
return (uniques == 0);
}
回答7:
I have implemented this before with a simple array of letter counts, e.g.:
unsigned char letter_frequency[26];
Then store that in a database table together with each word. Words that have the same letter frequency 'signature' are anagrams, and a simple SQL query then returns all anagrams of a word directly.
With some experimentation with a very large dictionary, I found no word that exceeded a frequency count of 9 for any letter, so the 'signature' can be represented as a string of numbers 0..9 (The size could be easily halved by packing into bytes as hex, and further reduced by binary encoding the number, but I didn't bother with any of this so far).
Here is a ruby function to compute the signature of a given word and store it into a Hash, while discarding duplicates. From the Hash I later build a SQL table:
def processword(word, downcase)
word.chomp!
word.squeeze!(" ")
word.chomp!(" ")
if (downcase)
word.downcase!
end
if ($dict[word]==nil)
stdword=word.downcase
signature=$letters.collect {|letter| stdword.count(letter)}
signature.each do |cnt|
if (cnt>9)
puts "Signature overflow:#{word}|#{signature}|#{cnt}"
end
end
$dict[word]=[$wordid,signature]
$wordid=$wordid+1
end
end
回答8:
Assign a unique prime number to the letters a-z
Iterate your word array, creating a product of primes based on the letters in each word.
Store that product in your word list, with the corresponding word.
Sort the array, ascending by the product.
Iterate the array, doing a control break at every product change.
回答9:
In C, I just implemented the following hash which basically does a 26-bit bitmask on whether the word in the dictionary has a particular letter in it. So, all anagrams have the same hash. The hash doesn't take into account repeated letters, so there will be some additional overloading, but it still manages to be faster than my perl implementation.
#define BUCKETS 49999
struct bucket {
char *word;
struct bucket *next;
};
static struct bucket hash_table[BUCKETS];
static unsigned int hash_word(char *word)
{
char *p = word;
unsigned int hash = 0;
while (*p) {
if (*p < 97 || *p > 122) {
return 0;
}
hash |= 2 << (*p - 97);
*p++;
}
return hash % BUCKETS;
}
Overloaded buckets created and added as linked list, etc. Then just write a function that makes sure that the words that match the hash value are the same length and that the letters in each are 1 to 1 and return that as a match.
回答10:
I will generate the hasmap based on the sample word and the rest of the alphabets I won't care.
For example if the word is "car" my hash table will be like this: a,0 b,MAX c,1 d,MAX e,MAX ... .. r,2 . As a result any has greater than 3 will consider as not matching
(more tuning...) And my comparison method will compare the hash total within the hash calculation itself. It won't continue once it can identify the word is not equal.
public static HashMap<String, Integer> getHashMap(String word) {
HashMap<String, Integer> map = new HashMap<String, Integer>();
String[] chars = word.split("");
int index = 0;
for (String c : chars) {
map.put(c, index);
index++;
}
return map;
}
public static int alphaHash(String word, int base,
HashMap<String, Integer> map) {
String[] chars = word.split("");
int result = 0;
for (String c : chars) {
if (c.length() <= 0 || c.equals(null)) {
continue;
}
int index = 0;
if (map.containsKey(c)) {
index = map.get(c);
} else {
index = Integer.MAX_VALUE;
}
result += index;
if (result > base) {
return result;
}
}
return result;
}
Main method
HashMap<String, Integer> map = getHashMap(sample);
int sampleHash = alphaHash(sample, Integer.MAX_VALUE, map);
for (String s : args) {
if (sampleHash == alphaHash(s, sampleHash, map)) {
System.out.print(s + " ");
}
}
回答11:
Anagrams can be found in following way:
- Length of word should match.
- Perform addition of each character in terms of integer value. This sum will match if you perform same on anagram.
- Perform multiplication of each character in terms of integer value. Evaluated value will match if you perform same on anagram.
So I thought through above three validations, we can find anagrams. Correct me if I'm wrong.
Example: abc cba
Length of both words is 3.
Sum of individual characters for both words is 294.
Prod of individual characters for both words is 941094.
回答12:
Just want to add simple python solution in addition to the other useful answers:
def check_permutation_group(word_list):
result = {}
for word in word_list:
hash_arr_for_word = [0] * 128 # assuming standard ascii
for char in word:
char_int = ord(char)
hash_arr_for_word[char_int] += 1
hash_for_word = ''.join(str(item) for item in hash_arr_for_word)
if not result.get(hash_for_word, None):
result[str(hash_for_word)] = [word]
else:
result[str(hash_for_word)] += [word]
return list(result.values())
回答13:
python code:
line = "man car kile arc none like"
hmap = {}
for w in line.split():
ws = ''.join(sorted(w))
try:
hmap[ws].append(w)
except KeyError:
hmap[ws] = [w]
for i in hmap:
print hmap[i]
output:
['car', 'arc']
['kile', 'like']
['none']
['man']
回答14:
JavaScript version. using hashing.
Time Complexity: 0(nm) , where n is number of words, m is length of word
var words = 'cat act mac tac ten cam net'.split(' '),
hashMap = {};
words.forEach(function(w){
w = w.split('').sort().join('');
hashMap[w] = (hashMap[w]|0) + 1;
});
function print(obj,key){
console.log(key, obj[key]);
}
Object.keys(hashMap).forEach(print.bind(null,hashMap))
来源：https://stackoverflow.com/questions/396005/algorithm-for-grouping-anagram-words