2012/1/4 Hadley Wickham <had...@rice.edu>:
>> NumericVector:::iterator is actually alias to double*. Here is a trick
>> (probably does not work on not gcc compilers):
>
> Ah, interesting - thanks!
I'm coming late to the party but ... I was able to squeeze a couple
of milliseconds from the computation by expressing the counts as
integers and ensuring that I did not copy x by using an Eigen::Map.
It may well be that an Rcpp::NumericVector will not be a copy in this
case but I have found it difficult to determine exactly when an Rcpp
vector is going to be copied. With Eigen I can ensure that the
original storage in R will be used for the vector.
count_eigen <- cxxfunction(signature(x="numeric", binwidth="numeric",
origin="numeric", nbins="integer"), '
double binwidth_ = ::Rf_asReal(binwidth), origin_ = ::Rf_asReal(origin);
Eigen::VectorXi counts(::Rf_asInteger(nbins));
Eigen::Map<Eigen::VectorXd> x_(as<Eigen::Map<Eigen::VectorXd> >(x));
int n = x_.size();
counts.setZero();
for (int i = 0; i < n; i++) counts[((x_[i] - origin_) / binwidth_)]++;
return wrap(counts);
', plugin="RcppEigen")
As you see, I use ::Rf_asReal() and ::Rf_asInteger() for conversion to
scalar doubles or scalar integers. Those functions are part of the R
API and are fast and general.
With this version I get a minor speedup
> microbenchmark(operator = count_bin(x, binwidth, origin, nbins = n),
+ iterator = count_bini(x, binwidth, origin, nbins = n),
+ eigen = count_eigen(x, binwidth, origin, nbins = n)
+ )
Unit: milliseconds
expr min lq median uq max
1 eigen 145.6456 145.7102 145.7536 145.8210 151.0456
2 iterator 153.3059 153.3603 153.4182 153.6056 155.3246
3 operator 156.2418 156.7063 156.7637 156.8982 159.8635
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