Question

I am using this code to generate a random permutation of a vector using a variation of Fisher-Yates randomization algorithm (I am going from the first element to the last, not the other way around). I am using a boost::random::mt11213b RNG globally in a program that is seeded with generator.seed(time(NULL)); when program starts, hence a wrapper singleton RandomNumber here.

boost::random::uniform_int_distribution<unsigned long> 
    distribution(0, vec.size()-1);

for (unsigned long i=0;i<vec.size();i++)
    std::swap(vec[i], vec[distribution(RandomNumber::getInstance().generator)]);

Some experiments have led me to believe that there may be an issue in this algorithm, in a nutshell. This is what I did

1. Created a vector of integer with length 100
2. Filled first 75 elements with 0 and the last 25 with 1
3. Shuffled an array.
4. Took the first 5 elements from the list and summed them.

I repeated this procedure a few thousand times (with a loop, not by hand :)) each time starting with a fresh vector. Then I computed the arithmetic mean of the sums and it came about 0.98 instead of the expected 1.25.

The funny thing is that if I start with a vector that has been shuffled once with the same algorithm instead of an ordered one, the result increases to 1.22 and if I don’t discard the vector on each iteration but rather just shuffle it again, the result is around 1.25that is the expected value.

I am unsure as to what could be wrong. The algorithm looks sound, the only thing that I can think of that could have gone wrong is the seeding phase and the

boost::random::uniform_int_distribution<unsigned long> 
    distribution(0, vec.size()-1);

line that is called each time before a vector is shuffled (perhaps it should only be called once a program but that doesn’t make mush sense)

Any help will be greatly appreciated!

Answer 1

If I had to take a guess as to the cause, you’re not changing the distribution size each time around the loop. The Art of Computer Programming algorithm is here.

Once you shuffle up to n elements, you don’t want to touch the first n again, because repeated applications of pseudo random numbers don’t making things more random, they make them less random.