Here's some code for sampling without replacement based on Algorithm 3.4.2S of Knuth's book Seminumeric Algorithms.

void SampleWithoutReplacement
(
    int populationSize,    // size of set sampling from
    int sampleSize,        // size of each sample
    vector<int> & samples  // output, zero-offset indicies to selected items
)
{
    // Use Knuth's variable names
    int& n = sampleSize;
    int& N = populationSize;

    int t = 0; // total input records dealt with
    int m = 0; // number of items selected so far
    double u;

    while (m < n)
    {
        u = GetUniform(); // call a uniform(0,1) random number generator

        if ( (N - t)*u >= n - m )
        {
            t++;
        }
        else
        {
            samples[m] = t;
            t++; m++;
        }
    }
}
There is a more efficient but more complex method by Jeffrey Scott Vitter in "An Efficient Algorithm for Sequential Random Sampling," ACM Transactions on Mathematical Software, 13(1), March 1987, 58-67

Knuth's algorithm S
TaskKnuth's algorithm S
You are encouraged to solve this task according to the task description, using any language you may know.
This is a method of randomly sampling n items from a set of M items, with equal probability; where M >= n and M, the number of items is unknown until the end. This means that the equal probability sampling should be maintained for all successive items > n as they become available (although the content of successive samples can change).
The algorithm
Select the first n items as the sample as they become available;
For the i-th item where i > n, have a random chance of n/i of keeping it. If failing this chance, the sample remains the same. If not, have it randomly (1/n) replace one of the previously selected n items of the sample.
Repeat #2 for any subsequent items.
The Task
Create a function s_of_n_creator that given n the maximum sample size, returns a function s_of_n that takes one parameter, item.
Function s_of_n when called with successive items returns an equi-weighted random sample of up to n of its items so far, each time it is called, calculated using Knuths Algorithm S.
Test your functions by printing and showing the frequency of occurrences of the selected digits from 100,000 repetitions of:
Use the s_of_n_creator with n == 3 to generate an s_of_n.
call s_of_n with each of the digits 0 to 9 in order, keeping the returned three digits of its random sampling from its last call with argument item=9.
Note: A class taking n and generating a callable instance/function might also be used.
Reference
The Art of Computer Programming, Vol 2, 3.4.2 p.142