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random_sample
PrototypeRandom_sample is an overloaded name; there are actually two random_sample functions.template <class InputIterator, class RandomAccessIterator> Random AccessIterator random_sample(InputIterator first, InputIterator last, RandomAccessIterator ofirst, RandomAccessIterator olast) template <class InputIterator, class RandomAccessIterator, class RandomNumberGenerator> random_sample(InputIterator first, InputIterator last, RandomAccessIterator ofirst, RandomAccessIterator olast, RandomNumberGenerator& rand) DescriptionRandom_sample randomly copies a sample of the elements from the range [first, last) into the range [ofirst, olast). 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 might appear in any order: relative order within the input range is not guaranteed to be preserved. [2]Random_sample copies n elements from [first, last) to [ofirst, olast), where n is min(last - first, olast - ofirst). The return value is ofirst + n. 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 backward-compatibility 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 are copied from the input range to the output range.Exampleint main() { const int N = 10; const int n = 4; int A[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}; int B[n]; random_sample(A, A+N, B, B+n); copy(B, B + n, ostream_iterator<int>(cout, " ")); // The printed value might be 1 6 3 5, // or any of 5039 other possibilities. } Notes[1] This is "Algorithm R" from section 3.4.2 of Knuth (D. E. Knuth, The Art of Computer Programming. Volume 2: Seminumerical Algorithms, second edition. Addison-Wesley, 1981). Knuth credits Alan Waterman. Note that there are N! / n! / (N - n)! ways of selecting a sample of n elements from a range of N elements. Random_sample yields uniformly distributed results; that is, the probability of selecting any particular element is n / N, and the probability of any particular sampling (not considering order of elements) is n! * (N - n)! / N!. [2] If preservation of the relative ordering within the input range is important for your application, you should use random_sample_n instead. The main restriction of random_sample_n is that the input range must consist of Forward Iterators, rather than Input Iterators. See alsorandom_shuffle, random_sample_n, Random Number GeneratorCopyright © 1999 Silicon Graphics, Inc. All Rights Reserved. TrademarkInformation
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