std::mt19937_64 mt{rd()}; template<typename T>T gen_random(T min, T max) { std::uniform_int_distribution<T>uid(min, max);returnuid(mt); }voidvector_sort_execution_par(constint&len) { std::vector<std::uint32_t>vec;for(inti =0; i < len; i++) { vec.push_back(gen_rando...
#include <chrono>#include <random>#include<iostream>int main() {std::random_device rd; // 创建一个std::random_device对象unsigned int seed = rd(); // 生成一个随机的种子值std::mt19937 engine(seed); // 使用随机的种子值创建一个伪随机数生成器 std::cout<<engine(); return 0;} 方法2:...
#define END 60000 static random_device rd; static mt19937 gen(rd()); static uniform_int_distribution<> random(1, NUM - 2); static QList<QSharedPointer<Test>> list1; static std::vector<QSharedPointer<Test>> vector1; void init() { for (int i = 0; i < NUM; ++i) { auto v =...
std::random_device rd; // 真随机数生成器std::mt19937 gen(rd()); // 以真随机数为种子的Mersenne Twister引擎std::uniform_int_distribution<> dis(1, 6); // 均匀分布for (int n=0; n<10; ++n)std::cout << dis(gen) << ' '; // 生成1到6之间的随机数 在上面的示例中,我们使用std...
源代码我计算由uniform_real_distribution生成的10亿个数字之和 #include <iostream> #include <random> using std::cout; using std::endl; using std::mt19937; using std::random_device; using std::uniform_real_distribution; int main() { random_device rd; mt19937 gen(rd()); uniform_real...
(seed_data), std::ref(rd)); std::seed_seqseq(std::begin(seed_data), std::end(seed_data)); std::mt19937engine(seq); uuids::uuid_random_generatorgen(&engine); std::set<uuids::uuid> ids{uuid{},gen(),gen(),gen(),gen()};assert(ids.size() == 5);assert(ids.find(uuid{}...
(); } std::random_device rd; std::mt19937_64 mt{rd()}; template <typename T> T gen_random(T min, T max) { std::uniform_int_distribution<T> uid(min, max); return uid(mt); } void log_file_par(const std::string &file_name, const int loops) { std::fstream w_file(file_...
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...::random_device rd; std::mt19937 gen(rd()); std::uniform_real_distribution dis(-1.0f, 1.0f);...测试结果这是对应的测试结果: C/C++代码 AVX2代码不使用缺省编译器优化 第一次:347ms第二次:298ms第三次:296ms 第一次:154ms第二次:153ms第三次:151ms 使用缺省编译器优化...如果不用...