[英]Not proper use of Random Generator for Gamma distribution draws in Rcpp
I wrote the following code in Rcpp, for generating Gamma Distribution random variables, however each time that I run it I take the same output.我在 Rcpp 中编写了以下代码,用于生成 Gamma 分布随机变量,但是每次运行它时,我都会得到相同的输出。 I read that in order to have different realizations I have to use a random generator as explained here Why is this random generator always output the same number
我读到,为了有不同的实现,我必须使用一个随机生成器,如解释here 为什么这个随机生成器总是输出相同的数字
The code that I use is the following, am I doing something wrong??我使用的代码如下,我做错了什么吗?? Basically, I feel that I use the exact same code as in the question posted, but for some reason in my case doesn't work.
基本上,我觉得我使用与发布的问题完全相同的代码,但由于某种原因,我的情况不起作用。
#include <RcppArmadilloExtensions/sample.h>
#include <random>
#include <iostream>
#include <math.h>
#include<Rmath.h>
using namespace Rcpp;
// [[Rcpp::depends(RcppArmadillo)]]
// [[Rcpp::export]]
double Gama_Draw_1(double a , double b){
std::default_random_engine generator;
std::gamma_distribution<double> d(a,b);
return d(generator);
}
// [[Rcpp::export]]
double Gama_Draw_2(double a , double b){
std::mt19937 prng{ std::random_device{}() };
std::gamma_distribution<double> d(a,b);
return d(prng);
}
// [[Rcpp::export]]
arma::vec Gama_Draw_3(double a , double b){
arma::vec S(10);
std::random_device rd;
std::mt19937 gen(rd());
std::gamma_distribution<> distrib(a, b);
for(int i=0; i<10; ++i){
S[i] = distrib(gen);
}
return S;
}
For example,例如,
Gama_Draw_1(1,1)
[1] 0.5719583
Gama_Draw_2(1,1)
[1] 1.065146
Gama_Draw_3(1,1)
[,1]
[1,] 1.0651459
[2,] 0.8683230
[3,] 2.7298295
[4,] 0.7930546
[5,] 0.3722135
[6,] 0.7317269
[7,] 0.2569218
[8,] 2.0916565
[9,] 0.9033717
[10,] 1.4198487
No matter how many times I run it I always get the same result.无论我运行多少次,我总是得到相同的结果。
The Rcpp package interfaces the random number generators (and special functions) from R, while properly interfacing the (high-quality) RNG inside R itself. Rcpp 包连接来自 R 的随机数生成器(和特殊函数),同时正确连接 R 本身内部的(高质量)RNG。
So I recommend you rely on that as it is in fact easy to use:所以我建议你依靠它,因为它实际上很容易使用:
> Rcpp::cppFunction("NumericVector mygamma(int n, double a, double b) { return Rcpp::rgamma(n, a, b); }")
> set.seed(123)
> mygamma(3, 0.5, 0.5)
[1] 0.05796143 1.14034608 0.00742452
> mygamma(3, 0.5, 0.5)
[1] 0.0753645 0.2474057 0.0151682
> set.seed(123)
> mygamma(3, 0.5, 0.5)
[1] 0.05796143 1.14034608 0.00742452
>
Resetting the seed gets us the same draw, not resetting gets us different draws.重置种子让我们获得相同的平局,而不是重置让我们获得不同的平局。 As it should.
正如它应该。 Also:
还:
> set.seed(123)
> rgamma(3, 0.5, 2.0)
[1] 0.05796143 1.14034608 0.00742452
>
Note that the second parameter for the Gamma is used as a reciprocal value between the R version and the compiled version.请注意,Gamma 的第二个参数用作 R 版本和编译版本之间的倒数。
Edit: For completeness, a working version with C++11 RNG follows.编辑:为了完整起见,下面是带有 C++11 RNG 的工作版本。 Obviously we cannot double the values from R.
显然,我们不能将 R 的值加倍。
#include <random>
#include <Rcpp.h>
std::mt19937 engine(123);
// [[Rcpp::export]]
Rcpp::NumericVector mygamma(int n, double a, double b) {
Rcpp::NumericVector v(n);
std::gamma_distribution<> gamma(a, b);
for (auto i=0; i<n; i++) {
v[i] = gamma(engine);
}
return v;
}
/*** R
mygamma(3, 0.5, 0.5)
mygamma(3, 0.5, 0.5)
*/
> Rcpp::sourceCpp("~/git/stackoverflow/68235476/answer.cpp")
> mygamma(3, 0.5, 0.5)
[1] 0.168918 1.254678 0.311767
> mygamma(3, 0.5, 0.5)
[1] 0.0451722 0.5485451 0.0443048
>
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.