Another, with mapply. But there we have to use rep since our mapply only
works on vector expressions.
inline double distance(double y, double x){ return pow( (y-x), 2.0 ) ; }
// [[Rcpp::export]]
NumericVector pdist6(double x, NumericVector ys) {
return mapply( ys, rep(x,ys.size()), distance ) ;
}
All this will become more fun with C++11 lambdas.
This one does not do as good as sapply:
Unit: microseconds
expr min lq median uq max
1 pdist1(0.5, ys) 24.692 25.0625 27.8755 28.1225 366.786
2 pdist2(0.5, ys) 30.595 30.9850 31.1210 31.4635 341.235
3 pdist3(0.5, ys) 262.306 262.7620 262.9740 263.8560 565.902
4 pdist4(0.5, ys) 264.561 264.9355 265.1850 266.8125 858.453
5 pdist5(0.5, ys) 15.700 16.1570 16.4030 17.2045 318.126
6 pdist6(0.5, ys) 31.264 31.5755 31.7225 32.3770 332.139
Romain
Le 17/11/12 17:40, Romain Francois a écrit :
Hi,
While there, consider this version based on sapply:
class Distance {
public:
typedef double result_type ;
Distance( double x_ ) : x(x_){}
inline double operator()(double y) const { return pow( (y-x), 2.0 )
; }
private:
double x;
} ;
// [[Rcpp::export]]
NumericVector pdist5(double x, NumericVector ys) {
return sapply( ys, Distance(x) ) ;
}
which here gives me quite good performance:
Unit: microseconds
expr min lq median uq max
1 pdist1(0.5, ys) 24.542 26.4825 27.8405 28.2305 326.597
2 pdist2(0.5, ys) 30.628 31.0030 31.1695 31.8715 608.207
3 pdist3(0.5, ys) 262.371 262.6280 262.9140 263.7840 563.796
4 pdist4(0.5, ys) 264.667 265.0150 265.2025 266.1770 580.343
5 pdist5(0.5, ys) 15.715 16.1375 16.3385 17.2195 318.412
Romain
Le 17/11/12 14:42, Hadley Wickham a écrit :
Hi all,
I've included what seems to be a simple application of Rcpp sugar
below, but I'm getting some very strange results. Any help would be
much appreciate!
Thanks,
Hadley
library(Rcpp)
library(microbenchmark)
# Compute distance between single point and vector of points
pdist1 <- function(x, ys) {
(x - ys) ^ 2
}
cppFunction('
NumericVector pdist2(double x, NumericVector ys) {
int n = ys.size();
NumericVector out(n);
for(int i = 0; i < n; ++i) {
out[i] = pow(ys[i] - x, 2);
}
return out;
}
')
ys <- runif(1e4)
all.equal(pdist1(0.5, ys), pdist2(0.5, ys))
library(microbenchmark)
microbenchmark(
pdist1(0.5, ys),
pdist2(0.5, ys)
)
# C++ version about twice as fast, presumably because it avoids a
# complete vector allocation.
# Sugar version:
cppFunction('
NumericVector pdist3(double x, NumericVector ys) {
return pow((x - ys), 2);
}
')
all.equal(pdist1(0.5, ys), pdist3(0.5, ys))
microbenchmark(
pdist1(0.5, ys),
pdist2(0.5, ys),
pdist3(0.5, ys)
)
# 10-fold slower?? Maybe it's because I'm using a double instead of
# a numeric vector?
cppFunction('
NumericVector pdist4(NumericVector x, NumericVector ys) {
return pow((x - ys), 2);
}
')
all.equal(pdist1(0.5, ys), pdist4(0.5, ys))
# Is this a bug in sugar? Should recycle to length of longest vector.
# Let's try flipping the order of operations:
cppFunction('
NumericVector pdist5(NumericVector x, NumericVector ys) {
return pow((ys - x), 2);
}
')
all.equal(pdist1(0.5, ys), pdist5(0.5, ys))
# Where are the missing values coming from??
--
Romain Francois
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