I'm currently trying to vectorize this nested for loop to save time during execution but it doesnt seem to work. What I want is to go through every cell of my matrix and check if the value is 0 or 1 then change the value based on a condition. This is the algorithm for the Forest-fire model
for (i in 1:nrow(X)) { for (j in 1:ncol(X)) { if (X[i, j] == 2) { if (runif(1) > (1 - a)^neighbours(X, i, j)) { B[i, j] <- 1 } } else if (X[i, j] == 1) { burning <- TRUE if (runif(1) < b) { B[i, j] <- 0 } } } } Here is the neighbours function :
neighbours <- function(A, i, j) { # calculate number of neighbours of A[i,j] that are infected # we have to check for the edge of the grid nbrs <- 0 # sum across row i - 1 if (i > 1) { if (j > 1) nbrs <- nbrs + (A[i-1, j-1] == 1) nbrs <- nbrs + (A[i-1, j] == 1) if (j < ncol(A)) nbrs <- nbrs + (A[i-1, j+1] == 1) } # sum across row i if (j > 1) nbrs <- nbrs + (A[i, j-1] == 1) nbrs <- nbrs + (A[i, j] == 1) if (j < ncol(A)) nbrs <- nbrs + (A[i, j+1] == 1) # sum across row i + 1 if (i < nrow(A)) { if (j > 1) nbrs <- nbrs + (A[i+1, j-1] == 1) nbrs <- nbrs + (A[i+1, j] == 1) if (j < ncol(A)) nbrs <- nbrs + (A[i+1, j+1] == 1) } return(nbrs) } And some code to make it work :
set.seed(3) X <- matrix(2, 21, 21) X[11, 11:13] <- 1 burning <- FALSE a= 0.2 b = 0.4 B <- X I've started by trying with sapply but couldn't get results back into the matrix and for the past hour I've been trying to use nested foreach loops
library(foreach) B <- foreach(i=1:nrow(X), .combine='cbind') %:% foreach(j=1:ncol(X), .combine='c') %do% { if (X[i, j] == 2) { if (runif(1) > (1 - a)^neighbours(X, i, j)) { 1 } } else if (X[i, j] == 1) { burning <- TRUE if (runif(1) < b) { 0 print(i) print(j) } } } But I'm only getting back the lines I need to change I'm not familiar with vectorization so perhaps I'm missing some basic steps !
https://stackoverflow.com/questions/66714556/r-vectorize-nested-for-loops-to-assign-new-values-to-a-matrix March 20, 2021 at 03:33AM
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