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random_sample_n
PrototypeRandom_sample_n is an overloaded name; there are actually two random_sample_n functions.template <class ForwardIterator, class OutputIterator, class Distance> OutputIterator random_sample_n(ForwardIterator first, ForwardIterator last, OutputIterator out, Distance n) template <class ForwardIterator, class OutputIterator, class Distance, class RandomNumberGenerator> OutputIterator random_sample_n(ForwardIterator first, ForwardIterator last, OutputIterator out, Distance n, RandomNumberGenerator& rand) DescriptionRandom_sample_n randomly copies a sample of the elements from the range [first, last) into the range [out, out + n). Each element in the input range appears at most once in the output range, and samples are chosen with uniform probability. [1] Elements in the output range appear in the same relative order as their relative order within the input range. [2]Random_sample copies m elements from [first, last) to [out, out + m), where m is min(last  first, n). The return value is out + m. The first version uses an internal random number generator, and the second uses a Random Number Generator, a special kind of function object, that is explicitly passed as an argument. DefinitionDefined in the standard header algorithm, and in the nonstandard backwardcompatibility header algo.h. This function is an SGI extension; it is not part of the C++ standard.Requirements on typesFor the first version:
Preconditions
ComplexityLinear in last  first. At most last  first elements from the input range are examined, and exactly min(n, last  first) elements are copied to the output range.Exampleint main() { const int N = 10; int A[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}; random_sample_n(A, A+N, ostream_iterator<int>(cout, " "), 4); // The printed value might be 3 5 6 10, // or any of 209 other possibilities. } Notes[1] This is "Algorithm S" from section 3.4.2 of Knuth (D. E. Knuth, The Art of Computer Programming. Volume 2: Seminumerical Algorithms, second edition. AddisonWesley, 1981). Knuth credits C. T. Fan, M. E. Muller, and I. Rezucha (1962) and T. G. Jones (1962). Note that there are N! / n! / (N  n)! ways of selecting a sample of n elements from a range of N elements. Random_sample_n yields uniformly distributed results; that is, the probability of selecting any particular element is n / N, and the probability of any particular sampling is n! * (N  n)! / N!. [2] In contrast, the random_sample algorithm does not preserve relative ordering within the input range. The other major distinction between the two algorithms is that random_sample_n requires its input range to be Forward Iterators and only requires its output range to be Output Iterators, while random_sample only requires its input range to be Input Iterators and requires its output range to be Random Access Iterators. See alsorandom_shuffle, random_sample, Random Number GeneratorCopyright © 1999 Silicon Graphics, Inc. All Rights Reserved. TrademarkInformation
