Hi,
NumericVector:::iterator is actually alias to double*. Here is a trick
(probably does not work on not gcc compilers):
> cxxfunction( , 'Rprintf( "%s", DEMANGLE(NumericVector::iterator)); ',
plugin = "Rcpp" )()
double*NULL
We did good on the NumericVector::operator[] to optimize it as much as
possible, in theory, with proper inlining we should not even see the
difference.
I don't see a wrong use of iterators or a way to make it fly faster still.
Romain
Le 04/01/12 15:16, Hadley Wickham a écrit :
Hi all,
Slightly less dense question (hopefully). In the code below I have
two versions of the same function - one uses operator[] and the other
uses iterators. Following the Rcpp introduction, I had expected the
iterator version to be substantially faster, but I'm only seeing a
minor improvement (~10%). Why doesn't using iterators help me much
here? Possible explanations:
* I'm using iterators incorrectly in my code
* Iterators help most when the vector access is sequential, and here
the counts index is bouncing all over the place, so I shouldn't expect
much improvement.
Any ideas would be much appreciated. Thanks!
Hadley
library(inline)
count_bin<- cxxfunction(signature(x = "numeric", binwidth =
"numeric", origin = "numeric", nbins = "integer"), '
int nbins_ = as<int>(nbins);
double binwidth_ = as<double>(binwidth);
double origin_ = as<double>(origin);
Rcpp::NumericVector counts(nbins_);
Rcpp::NumericVector x_(x);
int n = x_.size();
for(int i = 0; i< n; i++) {
counts[(int) ((x_[i] - origin_) / binwidth_)]++;
}
return counts;
', plugin = "Rcpp")
count_bini<- cxxfunction(signature(x = "numeric", binwidth =
"numeric", origin = "numeric", nbins = "integer"), '
int nbins_ = as<int>(nbins);
double binwidth_ = as<double>(binwidth);
double origin_ = as<double>(origin);
Rcpp::NumericVector counts(nbins_);
Rcpp::NumericVector x_(x);
int n = x_.size();
Rcpp::NumericVector::iterator x_i = x_.begin();
Rcpp::NumericVector::iterator counts_i = counts.begin();
for(int i = 0; i< n; i++) {
counts_i[(int) ((x_i[i] - origin_) / binwidth_)]++;
}
return counts;
', plugin = "Rcpp")
x<- rnorm(1e7, sd = 3)
origin<- min(x)
binwidth<- 1
n<- ceiling((max(x) - origin) / binwidth)
system.time(y1<- count_bin(x, binwidth, origin, nbins = n))
system.time(y2<- count_bini(x, binwidth, origin, nbins = n))
all.equal(y1, y2)
library(microbenchmark)
microbenchmark(
operator = count_bin(x, binwidth, origin, nbins = n),
iterator = count_bini(x, binwidth, origin, nbins = n))
)
# The real reason I'm exploring this is as a more efficient version
# of tabulate for doing equal bin counts. The Rcpp version is about 10x
# faster, mainly (I think) because it avoids creating a modified copy of the
# vector
system.time(y3<- tabulate((x - origin) / binwidth + 1, nbins = n))
all.equal(y1, y3)
--
Romain Francois
Professional R Enthusiast
http://romainfrancois.blog.free.fr
_______________________________________________
Rcpp-devel mailing list
Rcpp-devel@lists.r-forge.r-project.org
https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/rcpp-devel
