Le 09/12/2018 à 09:35, Mark Leeds a écrit :
Hi All: I wrote below and it works but I have a strong feeling there's a
better way to do it.
If performance is an issue, you can save few percents of cpu time by
using std::copy() instead of explicit for loop. Yet, for this operation
R's c() remains
For performance memcpy is probably fastest. This gives the same
performance a c().
// [[Rcpp::export]]
NumericVector mybar3(NumericVector x, double firstelem) {
NumericVector result(x.size() + 1);
result[0] = firstelem;
std::memcpy(result.begin()+1, x.begin(), x.size()*sizeof(double));
Small addendum: A large part of the performance gain in my example comes
from using NumericVector instead of std::vector. Which avoids a
conversion. An example using std::copy with Numeric vector runs in the
same time as the version using memcpy.
Jan
On 10-12-18 12:28, Jan van der Laan wrot
On 10 December 2018 at 13:04, Jan van der Laan wrote:
| Small addendum: A large part of the performance gain in my example comes
| from using NumericVector instead of std::vector. Which avoids a
| conversion. An example using std::copy with Numeric vector runs in the
| same time as the version
Le 10/12/2018 à 13:04, Jan van der Laan a écrit :
Small addendum: A large part of the performance gain in my example comes
from using NumericVector instead of std::vector. Which avoids a
conversion. An example using std::copy with Numeric vector runs in the
same time as the version using memcpy
Thanks to all for all the great repliies. I have to go through them but it
sounds like memcopy
is the best so I'll probably ending use that. Amazing to be part of this
list and receive all of
this wisdom.
Oh, as I said, the documentation on Rcpp is incredible but is there
anything discussing memo
Le 10/12/2018 à 16:48, Mark Leeds a écrit :
...
Oh, as I said, the documentation on Rcpp is incredible but is there
anything discussing memory because
I'm pretty lost on that. Thanks again.
Are you talking about this list archives?
http://lists.r-forge.r-project.org/pipermail/rcpp-devel/
Sergu
Hi,
What's the canonical way of creating a scalar SEXP in Rcpp? E.g. I would
like to store a single numeric / integer / complex ... value. I can create
an array with 1 element, but I couldn't find a way to do it for a scalar
(and I figured out later that a scalar is *not* the same as a 1element
ar
There are no scalars in R; there are vectors of length 1.
https://stat.ethz.ch/pipermail/r-help/2003-April/032010.html
Avi
On Mon, Dec 10, 2018 at 11:14 AM Wolf Vollprecht
wrote:
> Hi,
>
> What's the canonical way of creating a scalar SEXP in Rcpp? E.g. I would
> like to store a single numeric
The archives probably weren't what Mark was hoping for, though they are good
medicine. Reading the R documentation [1] is probably also good medicine that
Mark is hoping (in vain) to avoid. And then there is the topic of conventional
C++ memory handling, which is relevant but for which there are
On 10 December 2018 at 11:24, Avraham Adler wrote:
| There are no scalars in R; there are vectors of length 1.
Yep. An R feature, and gotcha if you come from somwhere else. See
R> a <- 1
R> is.vector(a)
[1] TRUE
R>
And while we can do this
R> Rcpp::cppFunction("double wolf(double x)
Your use of the term "array" in this context suggests you still don't have a
firm grasp of R data types. R arrays are vectors with dim attributes. What you
want is to create an R vector with 1 element, as shown in the quick reference.
[1]
[1] http://dirk.eddelbuettel.com/code/rcpp/Rcpp-quickref
Jeff,
On 10 December 2018 at 08:43, Jeff Newmiller wrote:
| Your use of the term "array" in this context suggests you still don't have a
firm grasp of R data types. R arrays are vectors with dim attributes. What you
want is to create an R vector with 1 element, as shown in the quick reference.
Hi Serguei: Thanks for the link but I was talking about how one would
understand that
my original Rcpp code was about as memory inefficient as one could possibly
get. I
knew it was ugly but not inefficient.
If there are specific threads that discuss the topic, those are fine also.
Mark
On Mon,
Thanks Jeff. I'll check those out. I recently found a gist of Gabor that
explains the relation
between Rcpp and the concept of pointers in C.
https://gist.github.com/ggrothendieck
On Mon, Dec 10, 2018 at 11:29 AM Jeff Newmiller
wrote:
> The archives probably weren't what Mark was hoping for, t
I did not intend to shoot anyone... but my own learning curve led me to skip
over the clear descriptions in the "Introduction to R" document because I
thought I already understood the terminology. True, it happened a long time
ago, but it took me an embarrassingly long time to grok this because
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