Passing an R function as a parameter to an RCpp function
I am trying to run something like
R
my_r_function <- function(input_a) {return(input_a**3)}
RunFunction(c(1,2,3), my_r_function)
CPP
#include <Rcpp.h>
using namespace Rcpp;
// [[Rcpp::export]]
NumericVector RunFunction(NumericVector a, Function func)
{
NumericVector b = NumericVector(a.size());
for(int i=0; i<a.size(); i++)
b[i] = func(a[i]);
return b;
}
How can I make "Function func" actually work in Rcpp?
PS I understand there are ways to do this without Rcpp (applicable for this example), but I'm just using that as an example to demonstrate what I'm looking for.
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You should be able to use the example in the link above to get your code to work; but you should also take note of Dirk's warning,
The function call is simple and tempting. It is also slow as it comes with overhead. And calling it repeatedly from within your C ++ code, possibly buried in multiple loops, is downright silly.
which can be demonstrated by slightly modifying the above code and comparing the two versions:
#include <Rcpp.h>
// [[Rcpp::export]]
Rcpp::NumericVector RunFunction(Rcpp::NumericVector a, Rcpp::Function func)
{
Rcpp::NumericVector b = func(a);
return b;
}
// [[Rcpp::export]]
Rcpp::NumericVector RunFunction2(Rcpp::NumericVector a, Rcpp::Function func)
{
Rcpp::NumericVector b(a.size());
for(int i = 0; i < a.size(); i++){
b[i] = Rcpp::as<double>(func(a[i]));
}
return b;
}
/*** R
my_r_function <- function(input_a) {return(input_a**3)}
x <- 1:10
##
RunFunction(x,my_r_function)
RunFunction2(x,my_r_function)
##
library(microbenchmark)
microbenchmark(
RunFunction(rep(1:10,10),my_r_function),
RunFunction2(rep(1:10,10),my_r_function))
Unit: microseconds
expr min lq mean median uq max neval
RunFunction(rep(1:10, 10), my_r_function) 21.390 22.9985 25.74988 24.0840 26.464 43.722 100
RunFunction2(rep(1:10, 10), my_r_function) 843.864 903.0025 1048.13175 951.2405 1057.899 2387.550 100
*/
Note that it RunFunction
is 40x faster than RunFunction2
: in the former, we only take overhead when called func
from C ++ code once, whereas in the latter case, we have to do an exchange for each element of the input vector. If you try to run this on longer vectors, I'm sure you will see significantly worse performance from RunFunction2
relatively RunFunction
. So, if you are going to call R functions from inside your C ++ code, you should try to use R-native vectorization (if possible) rather than making R function calls repeatedly in a loop, at least for fairly simple computations such as x**3
...
Also, if you are wondering why your code was not compiling, it was because of this line:
b[i] = func(a[i]);
You probably got the error
cannot convert 'SEXP to' Rcpp :: traits :: storage_type <14> :: type {aka double} in assignment
which I resolved by wrapping the return value func(a[i])
in the Rcpp::as<double>()
above. However, this is clearly not worth it because you end up with a much slower function.
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