在RcppArmadillo中将列向量乘以数字标量

在RcppArmadillo中将列向量乘以数字标量

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问题描述

我在使用RcppRcppArmadillo软件包编译此简单的c++代码时遇到了一些麻烦.以下面的简单示例为例,将矩阵的每一列乘以数字标量:

I am having some trouble compiling this simple c++ code using Rcpp and the RcppArmadillo package. Take the following simple example to multiply each column of a matrix by a numeric scalar:

code <- 'arma::mat out = Rcpp::as<arma::mat>(m);
for(int i = 0; i < out.n_cols; ++i){
  out.col(i) *= v;
}
return Rcpp::wrap( out );'

尝试使用...来编译它

Trying to compile this using...

require( RcppArmadillo )
armMult <- cxxfunction( signature( m = "numeric" , v = "numeric" ),
                        code , plugin = "RcppArmadillo" )

导致编译错误....

#error: no match for 'operator*=' in 'arma::Mat<eT>::col(arma::uword) [with eT = double, arma::uword = unsigned int](((unsigned int)i)) *= v'

但是,如果我们将numeric变量v换为2.0,如下所示....

However, if we swap the numeric variable v for 2.0 as below....

code <- 'arma::mat out = Rcpp::as<arma::mat>(m);
for(int i = 0; i < out.n_cols; ++i){
  out.col(i) *= 2.0; //Notice we use 2.0 instead of a variable
}
return Rcpp::wrap( out );'

它编译得很好....

armMult <- cxxfunction( signature(m="numeric"),
                        code,plugin="RcppArmadillo")

然后我们可以做...

And we can then do...

m <- matrix( 1:4 , 2 , 2 )

armMult( m )
     [,1] [,2]
[1,]    2    6
[2,]    4    8

我在这里想念什么?如何使用简单的数字标量来实现此目的.我希望能够通过像...这样的标量.

What am I missing here? How can I make this work with a simple numeric scalar. I would like to be able to pass a scalar like...

armMult( m , 2.0 )

并返回与上面相同的结果.

And return the same result as above.

推荐答案

如果要将矩阵 A 的每一列乘以向量的相应元素 x 然后试试这个:

If you want to multiply each column of a matrix A by the corresponding element of a vector x then try this:

Rcpp:::cppFunction(
    "arma::mat fun(arma::mat A, arma::rowvec x)
    {
        A.each_row() %= x;
        return A;
    }", depends = "RcppArmadillo"
)

fun(matrix(rep(1, 6), 3, 2), c(5, 1))

     [,1] [,2]
[1,]    5    1
[2,]    5    1
[3,]    5    1

这篇关于在RcppArmadillo中将列向量乘以数字标量的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持!

08-29 13:17