C++ 创建具有均值和标准差的高斯随机生成器
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Creating a Gaussian Random Generator with a mean and standard deviation
提问by coder_For_Life22
I am trying to create a one dimensional array and use a random number generator(Gaussian generator that generates a random number with means of 70 and a standard deviation of 10) to populate the array with at least 100 numbers between 0 and 100 inclusive.
我正在尝试创建一个一维数组并使用随机数生成器（高斯生成器，它生成一个均值为 70 且标准差为 10 的随机数）来填充数组，其中至少包含 0 到 100 之间的 100 个数字。
How would i go about doing this in C++?
我将如何在C++ 中做到这一点？
回答by Shafik Yaghmour
In C++11this is relatively straight forward using the random headerand std::normal_distribution(live example):
在C++11 中，使用随机标头和std::normal_distribution( live example)相对简单：
#include <iostream>
#include <iomanip>
#include <string>
#include <map>
#include <random>
int main()
{
std::random_device rd;
std::mt19937 e2(rd());
std::normal_distribution<> dist(70, 10);
std::map<int, int> hist;
for (int n = 0; n < 100000; ++n) {
++hist[std::round(dist(e2))];
}
for (auto p : hist) {
std::cout << std::fixed << std::setprecision(1) << std::setw(2)
<< p.first << ' ' << std::string(p.second/200, '*') << '\n';
}
}
If C++11is not an option than boostalso provides a library(live example):
如果C++11不是一个选项，那么boost还提供了一个库（现场示例）：
#include <iostream>
#include <iomanip>
#include <string>
#include <map>
#include <random>
#include <boost/random.hpp>
#include <boost/random/normal_distribution.hpp>
int main()
{
boost::mt19937 *rng = new boost::mt19937();
rng->seed(time(NULL));
boost::normal_distribution<> distribution(70, 10);
boost::variate_generator< boost::mt19937, boost::normal_distribution<> > dist(*rng, distribution);
std::map<int, int> hist;
for (int n = 0; n < 100000; ++n) {
++hist[std::round(dist())];
}
for (auto p : hist) {
std::cout << std::fixed << std::setprecision(1) << std::setw(2)
<< p.first << ' ' << std::string(p.second/200, '*') << '\n';
}
}
and if for some reason neither of these options is possible then you can roll your own Box-Muller transform, the code provided in the link looks reasonable.
如果由于某种原因这些选项都不可能，那么您可以推出自己的Box-Muller 变换，链接中提供的代码看起来很合理。
回答by samad montazeri
Use the Box Muller distribution (from here):
使用 Box Muller 分布（来自此处）：
double rand_normal(double mean, double stddev)
{//Box muller method
static double n2 = 0.0;
static int n2_cached = 0;
if (!n2_cached)
{
double x, y, r;
do
{
x = 2.0*rand()/RAND_MAX - 1;
y = 2.0*rand()/RAND_MAX - 1;
r = x*x + y*y;
}
while (r == 0.0 || r > 1.0);
{
double d = sqrt(-2.0*log(r)/r);
double n1 = x*d;
n2 = y*d;
double result = n1*stddev + mean;
n2_cached = 1;
return result;
}
}
else
{
n2_cached = 0;
return n2*stddev + mean;
}
}
you can read more at: wolframe math world
您可以在以下位置阅读更多信息：wolframe 数学世界
回答by Matteo Italia
In C++11 you would use the facilities provided by the <random>header; create a random engine (e.g. std::default_random_engineor std::mt19937, initialized with std::random_deviceif necessary) and a std::normal_distributionobject initialized with your parameters; then you can use them together to generate your numbers. Hereyou can find a full example.
在 C++11 中，您将使用<random>头文件提供的工具；创建一个随机引擎（例如std::default_random_engine或std::mt19937，std::random_device必要时用 初始化）和一个std::normal_distribution用你的参数初始化的对象；然后你可以一起使用它们来生成你的数字。在这里你可以找到一个完整的例子。
In previous versions of C++, instead, all you have is the "classic" C LCG (srand/rand), which just generates a plain integer distribution in the range [0, MAX_RAND]; with it you can still generate gaussian random numbers using the Box-Muller transform. (It might be useful to note that the C++11 GNU GCC libstdc++'s std::normal_distributionuses the Marsaglia polar methodas shown herein.).
相反，在以前版本的 C++ 中，您所拥有的只是“经典的” C LCG ( srand/ rand)，它只生成 [0, MAX_RAND] 范围内的普通整数分布；有了它，您仍然可以使用Box-Muller 变换生成高斯随机数。（注意 C++11 GNU GCC libstdc++std::normal_distribution使用这里显示的Marsaglia 极性方法可能很有用。）。
回答by yizzlez
With #include <random>
和 #include <random>
std::default_random_engine de(time(0)); //seed
std::normal_distribution<int> nd(70, 10); //mean followed by stdiv
int rarrary [101]; // [0, 100]
for(int i = 0; i < 101; ++i){
rarray[i] = nd(de); //Generate numbers;
}
