Slow computation in for loop
Yves Brostaux <brostaux.y at fsagx.ac.be> writes:
Dear members,
I'm using R to do some test computation on a set of parameters of a
function. This function is included in three for() loops, first one
for replications, and the remaining two cycling through possible
parameters values, like this :
for (k in replicates) {
data <- sampling from a population
for (i in param1) {
for (j in param2) {
result <- function(i, j, data)
}
}
}
With the 'hardest' set of parameters, a single computation of the
function take about 16s on an old Sun Sparc workstation with 64 Mb RAM
and don't access a single time to disk.
But when I launch the for() loops (which generate 220 function calls),
disk gets very sollicitated and the whole process takes as much as 8
to 10 hours, instead of the expected 1 hour.
What's wrong here ? Is there a thing I don't know about for() loops,
and a way to correct it ?
The problem with pseudocode: You didn't really overwrite the "result"
every time did you? I bet you stored it somewhere.
Two common causes of inefficiency are (a) that the stored objects may
be large and (b) some naive ways of storing the results involve
copying all preceding results, e.g.
list.of.results <- list()
for (.....){
result <- ...
list.of.results <- c(list.of.results, result)
}
The fix for (a) is to extract what you need and discard the rest
and for (b) to allocate the list up front with the proper length and
assign to list.of.results[[i]].
O__ ---- Peter Dalgaard Blegdamsvej 3 c/ /'_ --- Dept. of Biostatistics 2200 Cph. N (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~~~~~~~~~ - (p.dalgaard at biostat.ku.dk) FAX: (+45) 35327907