search for: ratio_sampling

This can be vectorized. Try ix <- seq_along(vec2) S_diff2 <- sapply(seq_len(N1-(N2-1)*ratio_sampling), \(j) sum((vec1[(ix-1)*ratio_sampling+j] - vec2[ix])**2)) On Sun, Jun 16, 2024 at 11:27?AM Laurent Rhelp <laurentRHelp at free.fr> wrote: > > Dear RHelp-list, > > I try to use the package comprehenr to replace a for loop by a list > comprehension. > > I wrote the...

...use it is very slower compared to the for loops. May you please explain to me why the list comprehension is slower in my case. Here is my example. I do the calculation of the square difference between the values of two vectors vec1 and vec2, the ratio sampling between vec1 and vec2 is equal to ratio_sampling. I have to use only the 500th value of the first serie before doing the difference with the value of the second serie (vec2). Thank you Best regards Laurent library(tictoc) library(comprehenr) ratio_sampling <- 500 ## size of the first serie N1 <- 70000 ## size of the second serie N2 &...

...use it is very slower compared to the for loops. May you please explain to me why the list comprehension is slower in my case. Here is my example. I do the calculation of the square difference between the values of two vectors vec1 and vec2, the ratio sampling between vec1 and vec2 is equal to ratio_sampling. I have to use only the 500th value of the first serie before doing the difference with the value of the second serie (vec2). Thank you Best regards Laurent library(tictoc) library(comprehenr) ratio_sampling <- 500 ## size of the first serie N1 <- 70000 ## size of the second serie N2 &...

...ugh such thinking speeds up Python also. A key step in that direction is to stop using lists directly for numeric calculations... use them to manage numeric vactors. In some cases you can switch to matrices or arrays to remove even more list manipulations from the script. library(microbenchmark) ratio_sampling <- 500 ## size of the first serie N1 <- 70000 ## size of the second serie N2 <- 100 ## mock data set.seed(123) vec1 <- rnorm(N1) vec2 <- runif(N2) dloop <- function( N1, M2, ratio_sampling, vec1, vec2 ) { S_diff2 <- numeric( N1-(N2-1)*ratio_sampling ) for( j in 1:len...

...ugh such thinking speeds up Python also. A key step in that direction is to stop using lists directly for numeric calculations... use them to manage numeric vactors. In some cases you can switch to matrices or arrays to remove even more list manipulations from the script. library(microbenchmark) ratio_sampling <- 500 ## size of the first serie N1 <- 70000 ## size of the second serie N2 <- 100 ## mock data set.seed(123) vec1 <- rnorm(N1) vec2 <- runif(N2) dloop <- function( N1, M2, ratio_sampling, vec1, vec2 ) { S_diff2 <- numeric( N1-(N2-1)*ratio_sampling ) for( j in 1:len...