我需要使用Boost Random生成随机数。
我试图遵循一般指南。
我解压缩了库的文件。所以如果我想使用库的类和objectj我应该怎么做?
首先我知道在程序中包含库。然后我必须编译库和program.cpp本身? (并且使用相同的编译器 - 我正在使用g ++)。
我正在使用ubuntu的虚拟盒子。这是我第一次使用图书馆,所以我真的不知道。
答案 0 :(得分:1)
我的案例的随机数必须是双倍而不仅仅是整数......
所以,你使用实数分布。
我不是这种"开始"最适合StackOverflow,但我会给你这个快速提示:
在您的Ubuntu虚拟框中:
sudo apt-get install libboost-all-dev
mkdir -pv ~/myproject
cd ~/myproject
使用您喜欢的编辑器创建文件。如果您没有,gedit main.cpp
或nano main.cpp
就是一个开头:
#include <boost/random.hpp>
#include <iostream>
int main() {
boost::random::mt19937 rng;
boost::random::uniform_real_distribution<double> gen(0.0, 1.0);
for (int i = 0; i < 10; ++i) {
std::cout << gen(rng) << "\n";
}
}
现在使用
编译它g++ -O2 -Wall -Wextra -pedantic main.cpp -o demo
该计划现已准备就绪: Live On Coliru
./demo
打印
0.814724
0.135477
0.905792
0.835009
0.126987
0.968868
0.913376
0.221034
0.632359
0.308167
以上是有效的,因为Boost随机库主要是标题。如果您想使用random_device
实现为随机生成器播种,该怎么办?
<强> Live On Coliru 强>
#include <boost/random.hpp>
#include <boost/random/random_device.hpp>
#include <iostream>
int main() {
boost::random::random_device seeder;
boost::random::mt19937 rng(seeder());
boost::random::uniform_real_distribution<double> gen(0.0, 1.0);
for (int i = 0; i < 10; ++i) {
std::cout << gen(rng) << "\n";
}
}
现在您还必须链接:使用
进行编译g++ -O2 -Wall -Wextra -pedantic main.cpp -o demo -lboost_random
现在每次运行输出都不同。
你根本不需要提升:
<强> Live On Coliru 强>
#include <random>
#include <iostream>
int main() {
std::random_device seeder;
std::mt19937 rng(seeder());
std::uniform_real_distribution<double> gen(0.0, 1.0);
for (int i = 0; i < 10; ++i) {
std::cout << gen(rng) << "\n";
}
}
使用
进行编译g++ -std=c++11 -O2 -Wall -Wextra -pedantic main.cpp -o demo
使用./demo
显示具有均值= 0和stddev = 1的全部分布:
<强> Live On Coliru 强>
#include <random>
#include <iostream>
#include <iomanip>
#include <chrono>
#include <boost/serialization/array_wrapper.hpp>
#include <boost/accumulators/accumulators.hpp>
#include <boost/accumulators/statistics.hpp>
namespace ba = boost::accumulators;
using Accum = ba::accumulator_set<double, ba::stats<ba::tag::variance, ba::tag::mean> >;
using Clock = std::chrono::high_resolution_clock;
using namespace std::chrono_literals;
static double identity(double d) { return d; }
template <typename Prng, typename Dist, typename F = double(double), size_t N = (1ull << 22)>
void test(Prng& rng, Dist dist, F f = &identity) {
Accum accum;
auto s = Clock::now();
for (size_t i = 0; i<N; ++i)
accum(f(dist(rng)));
std::cout
<< std::setw(34) << typeid(Dist).name()
<< ":\t" << ba::mean(accum)
<< " stddev: " << sqrt(ba::variance(accum))
<< " N=" << N
<< " in " << ((Clock::now()-s)/1.s) << "s"
<< std::endl;
}
int main() {
std::mt19937 rng(std::random_device{}());
auto shift = [](double shift) { return [=](double v) { return v + shift; }; };
auto scale = [](double scale) { return [=](double v) { return v * scale; }; };
std::cout << std::fixed << std::showpos;
test(rng, std::uniform_real_distribution<double>(-sqrt(3), sqrt(3)));
test(rng, std::weibull_distribution<double>(), shift(-1));
test(rng, std::exponential_distribution<double>(), shift(-1));
test(rng, std::normal_distribution<double>());
test(rng, std::lognormal_distribution<double>(0, log(0.5)), shift(-exp(pow(log(0.5),2)/2)));
test(rng, std::chi_squared_distribution<double>(0.5), shift(-0.5));
{
auto sigma = sqrt(6)/M_PI;
static constexpr double ec = 0.57721566490153286060;
test(rng, std::extreme_value_distribution<double>(-sigma*ec, sigma));
}
test(rng, std::fisher_f_distribution<double>(48, 8), shift(-(8.0/6.0)));
test(rng, std::student_t_distribution<double>(4), scale(sqrt(0.5)));
test(rng, std::student_t_distribution<double>(4), scale(sqrt(0.5)));
}
打印
St25uniform_real_distributionIdE: +0.000375 stddev: +1.000056 N=4194304 in +0.169681s
St20weibull_distributionIdE: +0.001030 stddev: +1.000518 N=4194304 in +0.385036s
St24exponential_distributionIdE: -0.000360 stddev: +1.000343 N=4194304 in +0.389443s
St19normal_distributionIdE: -0.000133 stddev: +1.000330 N=4194304 in +0.390235s
St22lognormal_distributionIdE: +0.000887 stddev: +1.000372 N=4194304 in +0.521975s
St24chi_squared_distributionIdE: -0.000092 stddev: +0.999695 N=4194304 in +1.233835s
St26extreme_value_distributionIdE: -0.000381 stddev: +1.000242 N=4194304 in +0.611973s
St21fisher_f_distributionIdE: -0.000073 stddev: +1.001588 N=4194304 in +1.326189s
St22student_t_distributionIdE: +0.000957 stddev: +0.998087 N=4194304 in +1.080468s
St22student_t_distributionIdE: +0.000677 stddev: +0.998786 N=4194304 in +1.079066s