> | for (i = 0; i < numObjects; i++) {
> | for (j = 0; j < numCodes; j++) {
> | dist = 0;
> | for (k = 0; k < numVars; k++) {
> | if (!ISNAN(data[i * numVars + k])) {
> | tmp = data[i * numVars + k] - codes[j * numVars + k];
>
> Why not drop data and codes and use sData1(i,k) - sData2(j,k) ?
Or better yet, just use the original code with NumericMatrix:
sData1[i * numVars + k] does the right thing.
I don't get any timing difference based on this change.
Using Rcpp sugar
(https://cran.r-project.org/package=Rcpp/vignettes/Rcpp-sugar.pdf),
and moving the call outside the loop, appears to do the right thing.
## modified example
## see edits here:
https://github.com/helmingstay/rcpp-timings/blob/master/diff/rcppdist.cpp#L24
git clone https://github.com/helmingstay/rcpp-timings
cd rcpp-timings/diff
R --vanilla < glue.R
best,
Christian
>
> That still doesn't explain the slowdowns though. Could you prepare a
> _complete_ yet minimal example along with mock data?
>
> Dirk
>
> --
> http://dirk.eddelbuettel.com | @eddelbuettel | e...@debian.org
>
--
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http://www.x14n.org
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