best way to pick a random subset from a collection?

Question

I have a set of objects in a Vector from which I'd like to select a random subset (e.g. 100 items coming back; pick 5 randomly). In my first (very hasty) pass I did an extremely simple and perhaps overly clever solution:

Vector itemsVector = getItems();

Collections.shuffle(itemsVector);
itemsVector.setSize(5);

While this has the advantage of being nice and simple, I suspect it's not going to scale very well, i.e. Collections.shuffle() must be O(n) at least. My less clever alternative is

Vector itemsVector = getItems();

Random rand = new Random(System.currentTimeMillis()); // would make this static to the class    

List subsetList = new ArrayList(5);
for (int i = 0; i < 5; i++) {
     // be sure to use Vector.remove() or you may get the same item twice
     subsetList.add(itemsVector.remove(rand.nextInt(itemsVector.size())));
}

Any suggestions on better ways to draw out a random subset from a Collection?

Answer 1

How much does remove cost? Because if that needs to rewrite the array to a new chunk of memory, then you've done O(5n) operations in the second version, rather than the O(n) you wanted before.

You could create an array of booleans set to false, and then:

for (int i = 0; i < 5; i++){
   int r = rand.nextInt(itemsVector.size());
   while (boolArray[r]){
       r = rand.nextInt(itemsVector.size());
   }
   subsetList.add(itemsVector[r]);
   boolArray[r] = true;
}

This approach works if your subset is smaller than your total size by a significant margin. As those sizes get close to one another (ie, 1/4 the size or something), you'd get more collisions on that random number generator. In that case, I'd make a list of integers the size of your larger array, and then shuffle that list of integers, and pull off the first elements from that to get your (non-colliding) indeces. That way, you have the cost of O(n) in building the integer array, and another O(n) in the shuffle, but no collisions from an internal while checker and less than the potential O(5n) that remove may cost.

Answer 2

I'd personal opt for your initial implementation: very concise. Performance testing will show how well it scales. I've implemented a very similar block of code in a decently abused method and it scaled sufficiently. The particular code relied on arrays containing >10,000 items as well.

Answer 3

Jon Bentley discusses this in either 'Programming Pearls' or 'More Programming Pearls'. You need to be careful with your N of M selection process, but I think the code shown works correctly. Rather than randomly shuffle all the items, you can do the random shuffle only shuffling the first N positions - which is a useful saving when N << M.

Knuth also discusses these algorithms - I believe that would be Vol 3 "Sorting and Searching", but my set is packed pending a move of house so I can't formally check that.

Answer 4

@Jonathan,

I believe this is the solution you're talking about:

void genknuth(int m, int n)
{    for (int i = 0; i < n; i++)
         /* select m of remaining n-i */
         if ((bigrand() % (n-i)) < m) {
             cout << i << "\n";
             m--;
         }
}

It's on page 127 of Programming Pearls by Jon Bentley and is based off of Knuth's implementation.

EDIT: I just saw a further modification on page 129:

void genshuf(int m, int n)
{    int i,j;
     int *x = new int[n];
     for (i = 0; i < n; i++)
         x[i] = i;
     for (i = 0; i < m; i++) {
         j = randint(i, n-1);
         int t = x[i]; x[i] = x[j]; x[j] = t;
     }
     sort(x, x+m);
     for (i = 0; i< m; i++)
         cout << x[i] << "\n";
}

This is based on the idea that "...we need shuffle only the first m elements of the array..."

Answer 5

Set<Integer> s = new HashSet<Integer>()
// add random indexes to s
while(s.size() < 5)
{
    s.add(rand.nextInt(itemsVector.size()))
}
// iterate over s and put the items in the list
for(Integer i : s)
{
    out.add(itemsVector.get(i));
}

Answer 6

I wrote an efficient implementation of this a few weeks back. It's in C# but the translation to Java is trivial (essentially the same code). The plus side is that it's also completely unbiased (which some of the existing answers aren't) - a way to test that is here.

It's based on a Durstenfeld implementation of the Fisher-Yates shuffle.

Answer 7

If you're trying to select k distinct elements from a list of n, the methods you gave above will be O(n) or O(kn), because removing an element from a Vector will cause an arraycopy to shift all the elements down.

Since you're asking for the best way, it depends on what you are allowed to do with your input list.

If it's acceptable to modify the input list, as in your examples, then you can simply swap k random elements to the beginning of the list and return them in O(k) time like this:

public static <T> List<T> getRandomSubList(List<T> input, int subsetSize)
{
    Random r = new Random();
    int inputSize = input.size();
    for (int i = 0; i < subsetSize; i++)
    {
        int indexToSwap = i + r.nextInt(inputSize - i);
        T temp = input.get(i);
        input.set(i, input.get(indexToSwap));
        input.set(indexToSwap, temp);
    }
    return input.subList(0, subsetSize);
}

If the list must end up in the same state it began, you can keep track of the positions you swapped, and then return the list to its original state after copying your selected sublist. This is still an O(k) solution.

If, however, you cannot modify the input list at all and k is much less than n (like 5 from 100), it would be much better not to remove selected elements each time, but simply select each element, and if you ever get a duplicate, toss it out and reselect. This will give you O(kn / (n-k)) which is still close to O(k) when n dominates k. (For example, if k is less than n / 2, then it reduces to O(k)).

If k not dominated by n, and you cannot modify the list, you might as well copy your original list, and use your first solution, because O(n) will be just as good as O(k).

As others have noted, if you are depending on strong randomness where every sublist is possible (and unbiased), you'll definitely need something stronger than java.util.Random. See java.security.SecureRandom.

Answer 8

This is a very similar question on stackoverflow.

To summarize my favorite answers from that page (furst one from user Kyle):

• O(n) solution: Iterate through your list, and copy out an element (or reference thereto) with probability (#needed / #remaining). Example: if k = 5 and n = 100, then you take the first element with prob 5/100. If you copy that one, then you choose the next with prob 4/99; but if you didn't take the first one, the prob is 5/99.
• O(k log k) or O(k²): Build a sorted list of k indices (numbers in {0, 1, ..., n-1}) by randomly choosing a number < n, then randomly choosing a number < n-1, etc. At each step, you need to recallibrate your choice to avoid collisions and keep the probabilities even. As an example, if k=5 and n=100, and your first choice is 43, your next choice is in the range [0, 98], and if it's >=43, then you add 1 to it. So if your second choice is 50, then you add 1 to it, and you have {43, 51}. If your next choice is 51, you add 2 to it to get {43, 51, 53}.

Here is some pseudopython -

# Returns a container s with k distinct random numbers from {0, 1, ..., n-1}
def ChooseRandomSubset(n, k):
  for i in range(k):
    r = UniformRandom(0, n-i)                 # May be 0, must be < n-i
    q = s.FirstIndexSuchThat( s[q] - q > r )  # This is the search.
    s.InsertInOrder(q ? r + q : r + len(s))   # Inserts right before q.
  return s

I'm saying that the time complexity is O(k²) or O(k log k) because it depends on how quickly you can search and insert into your container for s. If s is a normal list, one of those operations is linear, and you get k^2. However, if you're willing to build s as a balanced binary tree, you can get out the O(k log k) time.

Answer 9

I just posted an article about random sampling in my Java blog. It covers the algorithms mentioned here, plus implementations and analysis.

See http://eyalsch.wordpress.com/2010/04/01/random-sample/