std::ranges::sample
来自 cppreference.cn
| 定义于头文件 <algorithm> |
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| 调用签名 |
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| template< std::input_iterator I, std::sentinel_for<I> S, std::weakly_incrementable O, class Gen > |
(1) | (自 C++20 起) |
| template< ranges::input_range R, std::weakly_incrementable O, class Gen > requires (ranges::forward_range<R> || std::random_access_iterator<O>) && |
(2) | (自 C++20 起) |
1) 从序列
[first, last) 中选择 M = min(n, last - first) 个元素（不放回），使得每个可能的样本具有相等的出现概率，并将这些选定的元素写入以 out 开头的范围。 只有当
I 建模 std::forward_iterator 时，该算法才是稳定的（保留所选元素的相对顺序）。 如果 out 在
[first, last) 中，则行为未定义。在此页面上描述的类似函数的实体是 算法函数对象（非正式地称为 niebloids），即
内容 |
[编辑] 参数
| first, last | - | 迭代器-哨位对，定义从中进行采样的元素的范围（总体） |
| r | - | 从中进行采样的范围（总体） |
| out | - | 样本写入到的输出迭代器 |
| n | - | 要提取的样本数 |
| gen | - | 用作随机性来源的随机数生成器 |
[编辑] 返回值
一个等于 out + M 的迭代器，即结果样本范围的末尾。
[编辑] 复杂度
线性：𝓞(last - first)。
[编辑] 注解
此函数可能实现选择采样或 水塘采样。
[编辑] 可能的实现
struct 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 {}; |
[编辑] 示例
运行此代码
#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); } }
可能的输出
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 }[编辑] 参见
| (C++20) |
随机重排范围中的元素 (算法函数对象) |
| (C++17) |
从序列中选择 N 个随机元素 (函数模板) |
