Doubts on a Mersenne Twister Class

This is a discussion on Doubts on a Mersenne Twister Class within the C++ Programming forums, part of the General Programming Boards category; At a whim, I decided to convert Prelude's Mersenne Twister C library into a C++ class that could work with ...

  1. #1
    C++ Witch laserlight's Avatar
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    Doubts on a Mersenne Twister Class

    At a whim, I decided to convert Prelude's Mersenne Twister C library into a C++ class that could work with std::random_shuffle(). Naturally, I discovered how much I did not know about pseudorandom number generators

    My first question concerns working with random_shuffle(). Section 25.2.11 of the C++ Standard notes:
    random_shuffle() can take a particular random number generating function object rand such that if n is an argument for rand, with a positive value, that has type iterator_traits<RandomAccessIterator>::difference_ type, then rand(n) returns a randomly chosen value, which lies in the interval [0, n), and which has a type that is convertible to iterator_traits<RandomAccessIterator>::difference_ type.
    Josuttis used ptrdiff_t in The C++ Standard Library, and I decided to follow that (but with a typedef). Is there anything better?

    My next question is about generating random numbers within the range [0, n). From what I understand, Mersenne Twister does not suffer from the lower bit order problem, so it is safe to just take (random_number % n) ? I believe there will be a slight distribution problem (no longer uniform), but does it really matter? After all, Prelude's uniform deviate solution appears to suffer from the same problem, if my empirical evidence is correct.

    For example, suppose I actually have a generator that gives me random integers uniformly distributed in the range [0,7]. I want the range [0, 3). Using the modulo method, we have:
    Code:
    0 % 3 = 0
    1 % 3 = 1
    2 % 3 = 2
    3 % 3 = 0
    4 % 3 = 1
    5 % 3 = 2
    6 % 3 = 0
    7 % 3 = 1
    As such, there is now a probability of 0.375 each to get 0 and 1, and a probability of 0.25 to get 2. The distribution is no longer uniform. Switching to use a uniform deviate as a multiplier:
    Code:
    0 -> 0.0   => 0.0   * 3 = 0
    1 -> 0.125 => 0.125 * 3 = 0
    2 -> 0.25  => 0.25  * 3 = 0
    3 -> 0.375 => 0.375 * 3 = 1
    4 -> 0.5   => 0.5   * 3 = 1
    5 -> 0.625 => 0.625 * 3 = 1
    6 -> 0.75  => 0.75  * 3 = 2
    7 -> 0.875 => 0.875 * 3 = 2
    It seems to me that the resulting distribution is still non-uniform, and it must be so, since we are trying to squeeze a range of 8 numbers into a range of 3 numbers. There will surely be one number that has a frequency at least one less than the rest. My solution was to simply calculate the minimum number of numbers that will be out of range, and then just ignore them. For the above example, I would reduce the original range to [0, 6) by rejecting all numbers generated out of the range, and then use the modulo method on the first number generated within range. 6 is calculated by 7 - (7 % 3). Is this method correct, and is the overhead imposed worth it to fix the distribution problem (if that is a problem at all)?

    I have attached my code, feel free to comment if you see any other problems with it. I am still a little wary of my handling of unsigned long to Random::difference_type, since the latter is a typedef for a signed integer (ptrdiff_t).
    Attached Files Attached Files
    C + C++ Compiler: MinGW port of GCC
    Version Control System: Bazaar

    Look up a C++ Reference and learn How To Ask Questions The Smart Way

  2. #2
    aoeuhtns
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    The method you used is correct (assuming you made no mistakes in implementation), but not particularly practical. The innate inaccuracies of the random number generator will trump the very thin increased probability of the lower possible values. For example, if the range of your generator is [0, 2^32), and if you're taking modulo 6, you'll have an increased probability of getting 0,1,2,3, versus 4 and 5. But the difference between the probability of rolling a 1 and of rolling a 4 will be only 2^-32, assuming your random number generator is perfectly uniform.
    There are 10 types of people in this world, those who cringed when reading the beginning of this sentence and those who salivated to how superior they are for understanding something as simple as binary.

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