vectorization - Vectorise R code to randomly select 2 columns from each row -


does have suggestions of how vectorise code or otherwise speed up? i'm creating matrix, potentially large. in each row, want select 2 columns @ random, , flip them 0 1.

i cannot select same row , column number, i.e. diagonal of matrix 0 hence (1:n)[-j] in sample(). because changes each row, can't see way using vectorisation, parallelisation option here?

i use library(matrix) sparse matrix functionality.

library(matrix) n <- 100 m <- matrix(0, nrow = n, ncol = n)  for(j in 1:n) {     cols <- sample((1:n)[-j], 2) #choose 2 columns not equal      m[j, cols] <- 1 }

any ideas?

library(matrix) n <- 7  desired_output <- matrix(0, nrow = n, ncol = n) set.seed(1) for(j in 1:n) {   cols <- sample((1:n)[-j], 2) #choose 2 columns not equal    desired_output[j, cols] <- 1 }  # 7 x 7 sparse matrix of class "dgcmatrix" #                    # [1,] . . 1 . . . 1 # [2,] . . . . 1 1 . # [3,] . 1 . . . 1 . # [4,] . . . . 1 . 1 # [5,] 1 . . 1 . . . # [6,] 1 1 . . . . . # [7,] . 1 . . 1 . .  res <- matrix(0, nrow = n, ncol = n) set.seed(1) ind <- cbind(rep(1:n, each = 2), c(sapply(1:n, function(j) sample((1:n)[-j], 2)))) res[ind] <- 1  all.equal(res, desired_output) # [1] true

quick bench:

microbenchmark::microbenchmark(   op = {     desired_output <- matrix(0, nrow = n, ncol = n)     set.seed(1)     for(j in 1:n) {       cols <- sample((1:n)[-j], 2) #choose 2 columns not equal        desired_output[j, cols] <- 1     }   },   aurele = {     res <- matrix(0, nrow = n, ncol = n)     set.seed(1)     ind <- cbind(rep(1:n, each = 2), c(sapply(1:n, function(j) sample((1:n)[-j], 2))))     res[ind] <- 1   } )  # unit: milliseconds #    expr       min        lq      mean    median        uq       max neval cld #      op 10.240969 10.509384 11.065336 10.804949 11.044846 14.903377   100   b #  aurele  1.185001  1.258037  1.392021  1.363503  1.434818  4.553614   100

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