How can get random number in your range

Discussion in 'C Programming' started by bipi, Jul 27, 2007.

  1. bipi

    bipi Guest

    Dear all,

    I found function rand(), it can create random number but this function
    can not define the range of number which I want to get it, such as, I
    want to get random number in the range from 0 to 100 [0-100].

    Please help me,

    Many thanks,
    bipi, Jul 27, 2007
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  2. bipi

    jacob navia Guest

    13.16: How can I get random integers in a certain range?

    A: The obvious way,

    rand() % N /* POOR */

    (which tries to return numbers from 0 to N-1) is poor, because
    the low-order bits of many random number generators are
    distressingly *non*-random. (See question 13.18.) A better
    method is something like

    (int)((double)rand() / ((double)RAND_MAX + 1) * N)

    If you're worried about using floating point, you could use

    rand() / (RAND_MAX / N + 1)

    Both methods obviously require knowing RAND_MAX (which ANSI
    #defines in <stdlib.h>), and assume that N is much less than

    (Note, by the way, that RAND_MAX is a *constant* telling you
    what the fixed range of the C library rand() function is. You
    cannot set RAND_MAX to some other value, and there is no way of
    requesting that rand() return numbers in some other range.)

    If you're starting with a random number generator which returns
    floating-point values between 0 and 1, all you have to do to get
    integers from 0 to N-1 is multiply the output of that generator
    by N.

    References: K&R2 Sec. 7.8.7 p. 168; PCS Sec. 11 p. 172.
    jacob navia, Jul 27, 2007
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  3. See the FAQ, question 13.16.

    Peter J. Holzer, Jul 27, 2007
  4. It may be worth adding to this FAQ entry. rand() % N is poor even in
    some cases where the low-order bits are fine. If N does not divide
    RAND_MAX + 1, there will be a bias towards the numbers between 0 and
    RAND_MAX % N. The bias is small but grows as N gets closer to
    Ben Bacarisse, Jul 27, 2007
  5. [snip]

    jacob, thanks for making use of the FAQ, but surely it would be better
    to provide a *pointer* to it rather than just quoting it. And you
    forgot to mention what you were quoting, which fails to give credit to
    the FAQ and its author and potentially denies the OP the opportunity
    to read the rest of it (not that it's difficult to find).

    When I cite the FAQ, I usually just provide the FAQ's URL
    <> and a question number (13.16 in this case).

    Ben Bacarisse also made a good point about another problem with using
    one result from rand() to generate one number is a specified range.
    If there are, for example, 32768 possible results (RAND_MAX==32767),
    and you want numbers in the range 0..99, you can't possibly produce a
    uniform distribution, because 32768 is not a multiple of 100. In some
    applications, the slight skew is acceptable. If it isn't, you can
    reduce the range to 32700 possible values by rejecting any results in
    the range 32700..32767, and map 327 distinct rand() results to each
    value in the range 0..9. With a bit of work, the method can be
    generalized to arbitrary ranges and arbitrary values of RAND_MAX, as
    long as the desired range contains no more than RAND_MAX values. In
    the very worst case, this will reject fewer than half of the values
    returned by rand(), on average. This is random, of course, so you
    might wait arbitrarily long for a valid result.

    All this assumes that the rand() implementation is good enough that
    all this work is worthwhile.

    With some more arithmetic, I *think* there are ways to get a sequence
    of results in a specified range from a sequence of results in
    different specified range without rejecting anything. That's probably
    more than the OP needs, though, and I'm too lazy to work it out.
    Keith Thompson, Jul 27, 2007
  6. Or as I had already written in the random number speed thread a
    couple of days ago,

    "After that... find some power of 2 that is "just a bit" above a
    multiple of r (minimize the difference between the power of 2
    and the multiple), mask off that many bits from the returned
    random number, then reject the result if it is in the upper portion
    of the range (between the last full multiple and 2**N - 1)."

    I didn't specifically speak of bias the way Ben did, but that
    bias is the reason for the stated algorithm.
    Walter Roberson, Jul 27, 2007
  7. I think for best effect (fewest rejected random numbers), you'd want
    to miminize the *ratio* of the chosen power of 2 and a multiple of the
    size of your range, not the difference.

    Using a power of 2, I think, makes part of the operation quicker (you
    can use bitwise operations rather than division), but I think that by
    ignoring powers of 2 you can reject fewer of the input random numbers.

    I think you can avoid rejecting *any* input random numbers at the
    expense of some fairly heavy calculations. For example, if your
    desired range has 100 elements, you can treat a sequence of, say, 1000
    16-bit numbers as a single, very large, base-100 number, and grab one
    "digit" at a time. Generalizing this to arbitrarily long sequences is
    likely to require operations on arbitrarily large integer (bignums),
    and will be worthwhile only if the input random numbers are
    *extremely* expensive, or if you absolutely can't afford to reject too
    many in a row. But for such an application, it would probably be much
    easier to write your own random number generator in the first place.

    If RAND_MAX+1 is a multiple of the number of elements in your desired
    range, all these problems go away.
    Keith Thompson, Jul 27, 2007
  8. bipi

    Army1987 Guest

    This is already pointed out in the Web version of the FAQ.
    Also, if N is very small, if it isn't a divisor of RAND_MAX + 1,
    then RAND_MAX % N is better than if it is a divisor of it.
    For example, RAND_MAX % 2 alternates in some RNG, but RAND_MAX % 3
    uses all of the bits, so it is less predictable.
    Army1987, Aug 1, 2007
  9. I can't find it. I am happy to assume it is me and leave it at that.
    It is not a major issue, so if it is covered somewhere, that is more
    than adequate.
    Ben Bacarisse, Aug 1, 2007
  10. It's covered in the cited web page (which is question 13.16 of the FAQ):

    When N is close to RAND_MAX, and if the range of the random number
    generator is not a multiple of N (i.e. if (RAND_MAX+1) % N != 0),
    all of these methods break down: some outputs occur more often
    than others. (Using floating point does not help; the problem is
    that rand returns RAND_MAX+1 distinct values, which cannot always
    be evenly divvied up into N buckets.) If this is a problem, about
    the only thing you can do is to call rand multiple times,
    discarding certain values:

    unsigned int x = (RAND_MAX + 1u) / N;
    unsigned int y = x * N;
    unsigned int r;
    do {
    r = rand();
    } while(r >= y);
    return r / x;
    Keith Thompson, Aug 1, 2007
  11. bipi

    websnarf Guest

    The FAQ is deceptive and incomplete on this issue (and what the hell
    is drand48?). You can get a better explanation here:
    websnarf, Aug 2, 2007
  12. So it is. Must pay more attention.
    Ben Bacarisse, Aug 2, 2007
  13. How so?
    That's answered in question 13.21, which question 13.16 directly
    refers to.
    You raise 3 major points on that page.

    1. If the desired range does not divide the range of values returned
    by rand() a bias is introduced. FAQ 13.16 directly addresses that.

    2. The quality of rand() is often not very good. FAQ 13.16 also
    directly addresses that.

    3. There are problems if the desired range is wider than RAND_MAX. I
    think that's a valid criticism, though it certainly doesn't justify
    using the word "deceptive". (And in this particular context, the OP
    wanted range of 0..100, and RAND_MAX is guaranteed to be at least

    Your page appears to contain a lot of good information, and I hope to
    have the time to read the whole thing at some point, but it's probably
    more than would be appropriate for the clc FAQ. Adding a pointer to
    your page to the "additional links" page
    <> might be a good idea.

    Perhaps the FAQ has been updated since the last time you read it?
    Keith Thompson, Aug 2, 2007
  14. bipi

    CBFalconer Guest

    However people should be aware that some RNGs will never return the
    value 0. If this is so, and it is probably not documented, then
    you need some additional constructs to ensure even handed

    The lack of a zero is the result of a shift algorithm, which will
    lock up on the value 0.
    CBFalconer, Aug 2, 2007
  15. bipi

    websnarf Guest

    It gives an "answer", that is insufficient, and accompanied by
    incorrect explanations.
    Right, and its off topic for this newsgroup, right? I mean, what is
    it even doing in the FAQ?
    The FAQ suggests that if the range is small enough, then you are ok.
    This is untrue if accuracy is required, regardless of of what the
    range is. Specifically, if you take 1000 * (RAND_MAX / RANGE)
    samples, you will see a statistically significant deviation from the
    samples from the techniques they suggest and from a proper evenly
    distributed random sample. This is tantamount to the FAQ saying
    "don't sample this too much". On modern machines, given that RAND_MAX
    is commonly 32767, that number of samples is going to pretty much
    always be "smallish".
    It addresses the wrong things. In order to get more bits out of it,
    you have to call it a successive number of times, however, rand()
    implementations with too small of a state cannot produce all possible
    permutations of successive calls -- in fact successive calls are
    typically deterministic after 32 bits are output. For this reason, to
    be ideal, you need to use a PRNG that has a large enough state (such
    as the Mersenne Twister) to avoid this issue.

    If you know anything about computer graphics; using a small state
    rand() successively is like using (stochastic) dithering for images,
    while using a large state rand() is like having truly higher
    resolution. Having the higher resolution is generally better than
    dithering which is basically trying to gloss over the fundamental
    weakness of having lower resolution.
    It said there are problems if N is close to RAND_MAX (they should
    point out that its devastatingly wrong if RAND_MAX > N > RAND_MAX/2),
    which includes N being slightly larger than RAND_MAX. If it said "if
    N approaches RAND_MAX from below" or something like that you could
    almost forgive them, but they didn't.
    Give a man a fish vs teaching him to fish ...
    Well the FAQ could simply give my implementation of randbiased () and
    randrange () (the two together are not prohibitively long) and say
    that why it works is an exercise to the reader, or point to my page
    about it.
    Looks like more half-answer kind of things going on in there. It kind
    of reminds me of being forced to vote for the democratic nominee or
    the republican nominee instead of the candidate you actually want for
    the office.

    Sometimes the right answer matters.
    It was, but insufficiently.
    websnarf, Aug 2, 2007
  16. bipi

    Ernie Wright Guest

    I know something about graphics, and this analogy misses the mark.

    Higher resolution with uniform samples is like a bad PRNG with lots of
    bits. Stochastic sampling at lower resolution is like a good PRNG with
    fewer bits. Uniform sampling produces aliasing; higher resolution just
    changes the artifact wavelengths. Stochastic sampling randomizes (!)
    the unavoidable error of discrete sampling, trading aliasing for much
    less visually objectionable noise.

    They aren't mutually exclusive, either. Just like you'd want your PRNG
    with lots of bits to be good, you can stochastically sample at high
    resolution. But for CG, if I have to choose, I'll take sampling quality
    over raw resolution.


    So I think you need a different analogy.

    - Ernie
    Ernie Wright, Aug 2, 2007
  17. This is a recurring topic on sci.crypt, where it is sometimes applied
    to _true_ random sources (which can be expensive, at least in time) as
    well as PRNGs.

    IIRC the final outcome of the most recent round of discussions was to
    immediately use (uniformly) values below the highest exact multiple of
    R not over RAND_RANGE, as you noted upthread, and use 'rejected'
    values over that as base RAND_RANGE-N*R digits collected into an
    auxiliary accumulator, from which a value is taken when the
    digits=entropy in it get up to R and any overflow reused similarly.

    Unfortunately that group has been under a massive hipcrime attack for
    about two months now, which may make finding any useful information
    difficult, unless gooja has done an atypically good job of filtering.

    - formerly david.thompson1 || achar(64) ||
    David Thompson, Aug 26, 2007
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