[英]matrix of Uniform distribution Rcpp
I'm trying to create a function, which creates an matrix of size nxm, with entries of the uniform distribution, with the Rcpp package. 我正在尝试使用Rcpp包创建一个函数,该函数创建大小为nxm的矩阵,其中包含均匀分布的条目。 (I'm inexperienced using this package.)
(我没有使用此软件包的经验。)
library(Rcpp)
cppFunction('NumericMatrix rngCpp(const int n,const int m) {
NumericMatrix X(n, m);
X(_,0) = runif(n);
return X;
}')
set.seed(1)
rngCpp(4,5)
[,1] [,2] [,3] [,4] [,5]
[1,] 0.2655087 0 0 0 0
[2,] 0.3721239 0 0 0 0
[3,] 0.5728534 0 0 0 0
[4,] 0.9082078 0 0 0 0
Expected output 预期产量
set.seed(1)
matrix(runif(4*5), nrow=4, ncol = 5)
[,1] [,2] [,3] [,4] [,5]
[1,] 0.2655087 0.2016819 0.62911404 0.6870228 0.7176185
[2,] 0.3721239 0.8983897 0.06178627 0.3841037 0.9919061
[3,] 0.5728534 0.9446753 0.20597457 0.7698414 0.3800352
[4,] 0.9082078 0.6607978 0.17655675 0.4976992 0.7774452
Well with 很好
X(_,0) = runif(n);
you explicitly only assign to the first column. 您明确只分配给第一列。 So just loop over all m column doing the same.
因此,只需循环遍历所有m列即可。
Another (inside-baseball) way is to request a vector of n*m, and then set the dim
attributes of (n,m) to make it a matrix. 另一种(内部棒球)方式是请求向量n * m,然后设置(n,m)的
dim
属性使其成为矩阵。
Related, there is IIRC a constructor that would take that vector and re-dim it. 相关的是,有一个IIRC构造函数,它将采用该向量并对其进行暗化处理。
Edit: And that last approach is the simplest and works like this: 编辑:最后一种方法是最简单的,其工作方式如下:
R> cppFunction('NumericMatrix mu(int n, int m) {
NumericVector v = runif(n*m);
return NumericMatrix(n, m, v.begin()); }')
R> set.seed(1); mu(4,5)
[,1] [,2] [,3] [,4] [,5]
[1,] 0.265509 0.201682 0.6291140 0.687023 0.717619
[2,] 0.372124 0.898390 0.0617863 0.384104 0.991906
[3,] 0.572853 0.944675 0.2059746 0.769841 0.380035
[4,] 0.908208 0.660798 0.1765568 0.497699 0.777445
R>
Edit 2: Additional variants as F.Privé insists on arguing: 编辑2: F.Privé坚持认为的其他变体:
We can add these two: 我们可以添加以下两个:
// [[Rcpp::export]]
NumericMatrix mu3(int n, int m) {
return NumericMatrix(n, m, runif(n*m).begin());
}
// [[Rcpp::export]]
NumericMatrix mu4(int n, int m) {
NumericVector v = runif(n * m);
v.attr("dim") = Dimension(n, m);
return as<NumericMatrix>(v);
}
and then get 然后得到
R> N <- 1000; M <- 1000
R> microbenchmark::microbenchmark(
+ mu(N, M),
+ mu2(N, M),
+ mu3(N, M),
+ mu4(N, M),
+ rngCpp(N, M)
+ )
Unit: milliseconds
expr min lq mean median uq max neval
mu(N, M) 4.77894 4.98485 7.32315 5.18153 5.36468 33.2427 100
mu2(N, M) 3.99137 4.05308 5.43207 4.36296 4.57510 30.7335 100
mu3(N, M) 4.73176 5.01524 6.35186 5.17173 5.39541 31.7425 100
mu4(N, M) 3.99784 4.10052 4.72563 4.41176 4.60303 30.6166 100
rngCpp(N, M) 5.18726 5.60165 7.53171 5.83892 6.14315 34.5934 100
R>
In the OP's code, the assignment only happens for the 1st column index ie 0
. 在OP的代码中,分配仅发生在第一列索引即
0
。 We can loop through the column index and assign the uniform distribution values 我们可以遍历列索引并分配均匀分布值
cppFunction('NumericMatrix rngCpp(const int n,const int m) {
NumericMatrix X(n, m);
for(int i = 0; i < m; i++){
X(_,i) = runif(n);
}
return X;
}')
set.seed(1)
rngCpp(4,5)
# [,1] [,2] [,3] [,4] [,5]
#[1,] 0.2655087 0.2016819 0.62911404 0.6870228 0.7176185
#[2,] 0.3721239 0.8983897 0.06178627 0.3841037 0.9919061
#[3,] 0.5728534 0.9446753 0.20597457 0.7698414 0.3800352
#[4,] 0.9082078 0.6607978 0.17655675 0.4976992 0.7774452
Generally, I would use the second idea of Dirk: changing the dimension of a vector so that it becomes a matrix. 通常,我会使用Dirk的第二个想法:更改向量的尺寸,使其成为矩阵。
#include <Rcpp.h>
using namespace Rcpp;
// [[Rcpp::export]]
NumericVector mu2(int n, int m) {
NumericVector v = runif(n * m);
v.attr("dim") = Dimension(n, m);
return v;
}
// [[Rcpp::export]]
NumericMatrix mu(int n, int m) {
NumericVector v = runif(n*m);
return NumericMatrix(n, m, v.begin());
}
// [[Rcpp::export]]
NumericMatrix rngCpp(const int n,const int m) {
NumericMatrix X(n, m);
for(int i = 0; i < m; i++){
X(_,i) = runif(n);
}
return X;
}
/*** R
set.seed(1); mu(4, 5)
set.seed(1); mu2(4, 5)
N <- 1000; M <- 1000
microbenchmark::microbenchmark(
mu(N, M),
mu2(N, M),
rngCpp(N, M)
)
*/
Microbenchmark: 微基准测试:
Unit: milliseconds
expr min lq mean median uq max neval cld
mu(N, M) 7.627591 10.290691 16.05829 10.932174 11.416555 119.2110 100 a
mu2(N, M) 6.315160 6.590293 12.30125 8.510765 9.368282 117.8750 100 a
rngCpp(N, M) 8.330158 10.306584 16.14374 10.893767 11.398675 121.4233 100 a
So, mu2
is a bit faster because it doesn't make any copy. 因此,
mu2
速度更快,因为它不会进行任何复制。
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