我正在尝试找到一种有效的方法来为行和列的[[非连续集使用Rcpp子集矩阵:
m <- matrix(1:20000000, nrow=5000)
rows <- sample(1:5000, 100)
cols <- sample(1:4000, 100)
在R中,矩阵可以使用rows
和cols
向量直接子集:
matrix_subsetting <- function(m, rows, cols){ return(m[rows, cols]) } m[rows, cols] # or matrix_subsetting(m, rows, cols)
到目前为止,我能找到的最快的方法是:Rcpp
Rcpp::cppFunction("
NumericMatrix cpp_matrix_subsetting(NumericMatrix m, NumericVector rows, NumericVector cols){
int rl = rows.length();
int cl = cols.length();
NumericMatrix out(rl, cl);
for (int i=0; i<cl; i++){
NumericMatrix::Column org_c = m(_, cols[i]-1);
NumericMatrix::Column new_c = out(_, i);
for (int j=0; j<rl; j++){
new_c[j] = org_c[rows[j]-1];
}
}
return(out);
}")
但是相比之下,Rcpp版本要慢得多:
> microbenchmark::microbenchmark(matrix_subsetting(m, rows, cols), cpp_matrix_subsetting(m, rows, cols), times=500) Unit: microseconds expr min lq mean median uq max neval matrix_subsetting(m, rows, cols) 23.269 90.127 107.8273 130.347 135.3285 605.235 500 cpp_matrix_subsetting(m, rows, cols) 69191.784 75254.277 88484.9328 90477.448 95611.9090 178903.973 500
任何想法,至少要达到与Rcpp相当的速度?用我已经尝试过
RcppArmadillo
arma::mat::submat
函数,但是它比我的版本慢。解决方案:
cpp_matrix_subsetting
代替IntegerMatrix
实现NumericMatrix
功能。新基准:
> microbenchmark::microbenchmark(matrix_subsetting(m, rows, cols), cpp_matrix_subsetting(m, rows, cols), times=1e4)
Unit: microseconds
expr min lq mean median uq max neval
matrix_subsetting(m, rows, cols) 41.110 60.261 66.88845 61.730 63.8900 14723.52 10000
cpp_matrix_subsetting(m, rows, cols) 43.703 61.936 71.56733 63.362 65.8445 27314.11 10000
m
的矩阵integer
(不是double
所期望的NumericMatrix
,所以这会复制整个矩阵(这会花费很多时间)。例如,尝试使用m <- matrix(1:20000000 + 0, nrow=5000)
。