std::experimental::sample

Defined in header `<experimental/algorithm>`
template< class PopulationIterator, class SampleIterator, class Distance, class URBG > SampleIterator sample( PopulationIterator first, PopulationIterator last, SampleIterator out, Distance n, URBG&& g);	(1)	(library fundamentals TS)
template< class PopulationIterator, class SampleIterator, class Distance > SampleIterator sample( PopulationIterator first, PopulationIterator last, SampleIterator out, Distance n);	(2)	(library fundamentals TS v2)

Selects n elements from the sequence $[first; last)$ such that each possible sample has equal probability of appearance, and writes those selected elements into the output iterator out

If n is greater than the number of elements in the sequence, selects last-first elements.

The algorithm is stable only if PopulationIterator meets the requirements of LegacyForwardIterator

1) Random numbers are generated using the random number generator g.

2) Random numbers are generated using the per-thread engine.

Parameters

first, last	-	pair of iterators forming the range from which to make the sampling (the population)
out	-	the output iterator where the samples are written. Must not be in the $[first;last)$ range
n	-	number of samples to make
g	-	the random number generator used as the source of randomness
- `PopulationIterator` must meet the requirements of LegacyInputIterator.
- `SampleIterator` must meet the requirements of LegacyOutputIterator.
- `SampleIterator` must also meet the requirements of LegacyRandomAccessIterator if `PopulationIterator` doesn't meet LegacyForwardIterator
- `PopulationIterator`'s value type must be writeable to `out`
- `Distance` must be an integer type
- `URBG` must meet the requirements of UniformRandomBitGenerator and its return type must be convertible to `Distance`

Return value

Returns a copy of out after the last sample that was output, that is, end of the sample range.

Complexity

Linear in std::distance(first,last)

Notes

This function may implement selection sampling or reservoir sampling.

Example

Run this code

#include <iostream>
#include <random>
#include <string>
#include <iterator>
#include <experimental/algorithm>
 
int main()
{
    std::string in = "abcdefgh", out;
    std::experimental::sample(in.begin(), in.end(), std::back_inserter(out),
                              5, std::mt19937{std::random_device{}()});
    std::cout << "five random letters out of " << in << " : " << out << '\n';
}

Possible output:

five random letters out of abcdefgh : cdefg

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