I'm not completely sure but here is an idea,

Let A be some string of characters
Let B be some string of characters

To check if A and B has the same character you can do the following,

• radixSort(A) # using the ascii values of each letter in A -- O(n)


• radixSort(B) # using the ascii values of each letter in B -- O(n)


• compare each letter by letter in A and B

Not sure if thats what your trying to achieve.

You could use something similar to the counting sort algorithm. Count how many times each ASCII character appears in each string - if the counts are equal, then both strings contain the same characters.
e.g.

The problem with the prime number approach is you have a limited amount of characters you can check per number of bits available to store the multiplied primes. Assuming a 32-bit unsigned integer and 26 letters, 'z' would equal '101'. The string "zzzzzz" won't fit in 32-bits. In reality you only get like 5 characters that way. Even going up to 128 bits you only get up to 20 characters.

I'd go with SiCrane's or Hodgman's ideas.

Quote:Original post by Hodgman
You could use something similar to the counting sort algorithm. Count how many times each ASCII character appears in each string - if the counts are equal, then both strings contain the same characters.
e.g.*** Source Snippet Removed ***

this should be pretty efficient.

You can check first if the lengths of the strings are equal and if they are, you can use the same loop to count the occurrences.

If your goal is to find all possible words based on scrambled word by searching some sort of database of words, presented solution taken as is could be to slow.
In such a case I would recommend preprocessing your words database first in the following manner:

1. You take each word from the database, sort it first, and next copy it to other database, remembering original word and sorted one. This new database should be sorted by sorted names (sounds strange ;))
For example, if you have words: 'cat', 'dog', 'god', 'good', 'ape', that would be:
act->cat
aep->ape
dog->dog, god
doog->good

It is better to perform this step previously and next work on preprocessed database, as when comparing scrambled name you will not have to sort each word from database during comparison, so it will be much faster.
If you fear it will take too long, consider that it has to be done only once and if you won't do this such way, you will have to perform this task during each search.

2. When searching for true word from the scrambled one, this task will be just simple searching word from the database, what could be obtained either by some sort of logarithmic search, or by using hash-arrays, or by constructing tree from the database.

Good luck
Pomnico

Edit:
By database I don't mean SQL necessary.

Quote:Original post by Sneftel
Here's an idea: Quickcomp. (Quickeq? I can't think of a good name.) Like quicksort, but you sort the two arrays in parallel, and early-out if the partition sizes differ.

def partition(A):  if len(A) == 1: return  pivot = A[0]  cursor = 0  for i in range(len(A)):    if A <= pivot:      A[cursor],A = A,A[cursor]      cursor += 1  return cursordef quickcomp(A, B):  if len(A) != len(B): return false  if len(A) <= 1:     if len(A) == 0: return true    else: return (A[0] == B[0])  a_pivloc = partition(A)  b_pivloc = partition(B)  if not quickcomp(A[:a_pivloc-1], B[:a_pivloc-1]): return false  if not quickcomp(A[a_pivloc+1:], B[a_pivloc+1:]): return false  return true

Without thinking about it too hard, it seems like this should perform in average time O(n) if the sets differ, and O(n log n) if they are the same. My intuition is that that's the best you can do without non-comparison-based stuff.

That's a cool algorithm idea. I decided to implement it myself and one problem I ran into is that you need to ensure that the partitioning step picks the same item for both a and b. If it picks a different pivot then a different number of items will be less than it.

SiCrane mentioned my idea.