Call signature
(1) (since C++20) (2) (since C++20)1) Selects M = min(n, last - first) elements from the sequence [
first,
last)
(without replacement) such that each possible sample has equal probability of appearance, and writes those selected elements into the range beginning at out.
The algorithm is
stable(preserves the relative order of the selected elements) only if
I
models
std::forward_iterator.
The behavior is undefined if out is in [
first,
last)
.
The function-like entities described on this page are algorithm function objects (informally known as niebloids), that is:
An iterator equal to out + M, that is the end of the resulting sample range.
[edit] ComplexityLinear: ð(last - first).
[edit] NotesThis function may implement selection sampling or reservoir sampling.
[edit] Possible implementationstruct sample_fn { template<std::input_iterator I, std::sentinel_for<I> S, std::weakly_incrementable O, class Gen> requires (std::forward_iterator<I> or std::random_access_iterator<O>) && std::indirectly_copyable<I, O> && std::uniform_random_bit_generator<std::remove_reference_t<Gen>> O operator()(I first, S last, O out, std::iter_difference_t<I> n, Gen&& gen) const { using diff_t = std::iter_difference_t<I>; using distrib_t = std::uniform_int_distribution<diff_t>; using param_t = typename distrib_t::param_type; distrib_t D{}; if constexpr (std::forward_iterator<I>) { // this branch preserves "stability" of the sample elements auto rest{ranges::distance(first, last)}; for (n = ranges::min(n, rest); n != 0; ++first) if (D(gen, param_t(0, --rest)) < n) { *out++ = *first; --n; } return out; } else { // O is a random_access_iterator diff_t sample_size{}; // copy [first, first + M) elements to "random access" output for (; first != last && sample_size != n; ++first) out[sample_size++] = *first; // overwrite some of the copied elements with randomly selected ones for (auto pop_size{sample_size}; first != last; ++first, ++pop_size) { const auto i{D(gen, param_t{0, pop_size})}; if (i < n) out[i] = *first; } return out + sample_size; } } template<ranges::input_range R, std::weakly_incrementable O, class Gen> requires (ranges::forward_range<R> or std::random_access_iterator<O>) && std::indirectly_copyable<ranges::iterator_t<R>, O> && std::uniform_random_bit_generator<std::remove_reference_t<Gen>> O operator()(R&& r, O out, ranges::range_difference_t<R> n, Gen&& gen) const { return (*this)(ranges::begin(r), ranges::end(r), std::move(out), n, std::forward<Gen>(gen)); } }; inline constexpr sample_fn sample {};[edit] Example
#include <algorithm> #include <iomanip> #include <iostream> #include <iterator> #include <random> #include <vector> void print(auto const& rem, auto const& v) { std::cout << rem << " = [" << std::size(v) << "] { "; for (auto const& e : v) std::cout << e << ' '; std::cout << "}\n"; } int main() { const auto in = {1, 2, 3, 4, 5, 6}; print("in", in); std::vector<int> out; const int max = in.size() + 2; auto gen = std::mt19937{std::random_device{}()}; for (int n{}; n != max; ++n) { out.clear(); std::ranges::sample(in, std::back_inserter(out), n, gen); std::cout << "n = " << n; print(", out", out); } }
Possible output:
in = [6] { 1 2 3 4 5 6 } n = 0, out = [0] { } n = 1, out = [1] { 5 } n = 2, out = [2] { 4 5 } n = 3, out = [3] { 2 3 5 } n = 4, out = [4] { 2 4 5 6 } n = 5, out = [5] { 1 2 3 5 6 } n = 6, out = [6] { 1 2 3 4 5 6 } n = 7, out = [6] { 1 2 3 4 5 6 }[edit] See also randomly re-orders elements in a range
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