On Wed, Dec 14, 2016 at 12:23 PM, Johannes Kruisselbrink
wrote:
>
> Good point. Actually, I didn't even realize there were so many is-nan
> functions to choose from. But indeed, we used the R-core ISNAN function on
> doubles accessed via Rcpp.
Me either :)
Of course, the above isn't particular to
Take care to distinguish the R-core ISNAN macro, the R_IsNaN function,
and the Rcpp::isNaN template function. Asides from sugar-stuff, all
the examples discussed here so far address the performance of R-core
ISNAN, given doubles that are accessed via Rcpp, correct?
Good point. Actually, I didn
Hello, Amina.
Firstly, why are you even passing in a if b contains the proper dimensions.
It's not like you are using a.
Secondly, and more importantly, once you provided the r code, you can see
that this is **EXACTLY** the problem you asked on September 26 of this
year, see
http://lists.r-forge.
Hi Amina,
Looks like you want us to do your homework, right? ;)
Pay attention to subscript translation from R to C:
c[i-1, j, k-1] * b[i, j]
vs
c(i-1, j, k) *b(i, j)
Can you see the difference?
Best,
Serguei.
Le 14/12/2016 à 07:46, Amina Shahzadi a écrit :
Oh sorry. No. of columns in b and
> so skipping the entire object is not really an option.
Understood - just wanted to highlight the existence of Rcpp::is_nan /
Rcpp::any in case they were broadly relevant.
> Nevertheless, the question still remains why the rcpp isNaN call is so
> much slower.
Take care to distinguish the R-core
