simple problem. I want to check if the difference of two points (i, j) is greater than a threshold (diff). If the difference between the points exceeds the threshold the index should be returned and the next distance is measured but from the new datapoint. It is a simple cutofffilter where all datapoints under a predefined threshold are filtered. The only trick is, that the measurement is performed from always the "last" point (that was "far enough away" from the point before).
I first wrote it as two nested loops like:
x <- sample(1:100)
for(i in 1:(length(x)-1)){
for(j in (i+1):length(x)){
if(abs(x[i] - x[j]) >= cutoff) {
print(j)
i <- j # set the index to the current datapoint
break }
}}
This solution is kind of intuitive. But does not work proper. I think the assignment of i and j is not valid. The first loop just ignores to jump and loops through all datapoints.
Well, I did not want to waste time with debugging and just thought I can do the same with a recursive function. So I wrote it like:
checkCutOff.f <- function(x,cutoff,i = 1) {
options(expressions=500000)
# Loops through the data and comperes the temporally fixed point 'i with the looping points 'j
for(j in (i+1):length(x)){
if( abs(x[i] - x[j]) >= cutoff ){
break
}
}
# Recursive function to update the new 'i - stops at the end of the dataset
if( j<length(x) ) return(c(j,checkCutOff.f(x,cutoff,j)))
else return(j)
}
x<-sample(1:100000)
checkCutOff.f(x,1)
This code works. But I get a stack overflow with big datasets. That's why I ask myself if this code is efficient. For me is increasing limits etc. always a hint for inefficient code...
So my question is: What kind of solution is really efficient? Thanks!
You should avoid growing your return value with c
. That's inefficient. Allocate to the maximum size and subset to the needed size in the end.
Note that your function always includes length(x)
in your result, which is wrong:
set.seed(42)
x<-sample(1:10)
checkCutOff.f(x, 100)
#[1] 10
Here is an R solution with a loop:
checkCutOff.f1 <- function(x,cutoff) {
i <- 1
j <- 1
k <- 1
result <- integer(length(x))
while(j < length(x)) {
j <- j + 1
if (abs(x[i] - x[j]) >= cutoff) {
result[k] <- j
k <- k + 1
i <- j
}
}
result[seq_len(k - 1)]
}
all.equal(checkCutOff.f(x, 4), checkCutOff.f1(x, 4))
#[1] TRUE
#the correct solution includes length(x) here (by chance)
It's easy to translate to Rcpp:
#include <Rcpp.h>
using namespace Rcpp;
// [[Rcpp::export]]
IntegerVector checkCutOff_f1cpp(NumericVector x, double cutoff) {
int i = 0;
int j = 1;
int k = 0;
IntegerVector result(x.size());
while(j < x.size()) {
if (std::abs(x[i] - x[j]) >= cutoff) {
result[k] = j + 1;
k++;
i = j;
}
j++;
}
result = result[seq_len(k)-1];
return result;
}
Then in R:
all.equal(checkCutOff.f(x, 4), checkCutOff_f1cpp(x, 4))
#[1] TRUE
Benchmarks:
library(microbenchmark)
y <- sample(1:1000)
microbenchmark(
checkCutOff.f(y, 4),
checkCutOff.f1(y, 4),
checkCutOff_f1cpp(y, 4)
)
#Unit: microseconds
# expr min lq mean median uq max neval cld
# checkCutOff.f(y, 4) 3665.105 4681.6005 7798.41776 5323.068 6635.9205 41028.930 100 c
# checkCutOff.f1(y, 4) 1384.524 1507.2635 1831.43236 1769.031 2070.7225 3012.279 100 b
# checkCutOff_f1cpp(y, 4) 8.765 10.7035 26.40709 14.240 18.0005 587.958 100 a
I'm sure this can be improved further and more testing should be done.
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