Displaying 6 results from an estimated 6 matches for "sum_squares".
2024 Jun 16
2
slowness when I use a list comprehension
...- 70000
## size of the second serie
N2 <- 100
## mock data
set.seed(123)
vec1 <- rnorm(N1)
vec2 <- runif(N2)
## 1. with the "for" loops
## the square differences will be stored in a vector
S_diff2 <- numeric((N1-(N2-1)*ratio_sampling))
tic()
for( j in 1:length(S_diff2)){
? sum_squares <- 0
? for( i in 1:length(vec2)){
??? sum_squares = sum_squares + ((vec1[(i-1)*ratio_sampling+j] -
vec2[i])**2)
? }
? S_diff2[j] <- sum_squares
}
toc()
## 0.22 sec elapsed
which.max(S_diff2)
## 7857
## 2. with the lists comprehension
tic()
S_diff2 <- to_vec(for( j in 1:length(S_diff2...
2024 Jun 16
1
slowness when I use a list comprehension
...- 70000
## size of the second serie
N2 <- 100
## mock data
set.seed(123)
vec1 <- rnorm(N1)
vec2 <- runif(N2)
## 1. with the "for" loops
## the square differences will be stored in a vector
S_diff2 <- numeric((N1-(N2-1)*ratio_sampling))
tic()
for( j in 1:length(S_diff2)){
sum_squares <- 0
for( i in 1:length(vec2)){
sum_squares = sum_squares + ((vec1[(i-1)*ratio_sampling+j] -
vec2[i])**2)
}
S_diff2[j] <- sum_squares
}
toc()
## 0.22 sec elapsed
which.max(S_diff2)
## 7857
## 2. with the lists comprehension
tic()
S_diff2 <- to_vec(for( j in 1:length(S_diff2...
2024 Jun 16
1
slowness when I use a list comprehension
...data
> set.seed(123)
> vec1 <- rnorm(N1)
> vec2 <- runif(N2)
>
>
> ## 1. with the "for" loops
>
> ## the square differences will be stored in a vector
> S_diff2 <- numeric((N1-(N2-1)*ratio_sampling))
> tic()
> for( j in 1:length(S_diff2)){
> sum_squares <- 0
> for( i in 1:length(vec2)){
> sum_squares = sum_squares + ((vec1[(i-1)*ratio_sampling+j] -
> vec2[i])**2)
> }
> S_diff2[j] <- sum_squares
> }
> toc()
> ## 0.22 sec elapsed
> which.max(S_diff2)
> ## 7857
>
> ## 2. with the lists comprehen...
2024 Jun 16
1
slowness when I use a list comprehension
...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:length(S_diff2) ) {
sum_squares <- 0
for( i in 1:length(vec2)){
sum_squares <- (
sum_squares
+ (
vec1[ (i-1)*ratio_sampling+j ]
- vec2[i]
)**2
)
}
S_diff2[j] <- sum_squares
}
S_diff2
}
vloop <- function( N1, M2, ratio_sampling, vec1, vec2 ) {
S...
2024 Jun 16
1
slowness when I use a list comprehension
...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:length(S_diff2) ) {
sum_squares <- 0
for( i in 1:length(vec2)){
sum_squares <- (
sum_squares
+ (
vec1[ (i-1)*ratio_sampling+j ]
- vec2[i]
)**2
)
}
S_diff2[j] <- sum_squares
}
S_diff2
}
vloop <- function( N1, M2, ratio_sampling, vec1, vec2 ) {
S...
2009 Oct 01
1
Using optimize with array variables
Hello,
I am trying to figure out how to use optimize() with array variables
as inputs. I have a for loop in the function definition:
SS <- function(int,slo,x,y){
for(i in 1:length(x)) ((int+slo*x[i])-y[i])^2->squares[i]
sum(squares)->>sum_squares
output_txt = c ("The sum of squares is", sum_squares)
print(output_txt, quote=FALSE)}
Even assuming I make x and y single-integer variables, for example:
optimize(SS, c(0,1), tol = 0.0001, x=1, y=1, slo=1)
I get the error:
Error in optimize(SS, c(0, 1), tol = 1e-04, x = 1, y = 1, slo...