Slow computation in for loop
First of all, thank you for your response.
I actually have to refine my pseudocode. 'result' is a numerical vector of
length 7, and is binded with whole results through an rbind() :
for (k in replicates) {
data <- sampling from a population
for (i in param1) {
for (j in param2) {
result <- function(i, j, data)
all.results <- rbind(all.results, result)
}
}
}
all.result is at most a 220 rows and 7 columns data frame, which doesn't
seem to be big enough to explain such a slow computation.
Moreover, previous computations with a sample size of 100, which took
individually about 4 seconds at most, ran effectively in a little bit more
than 15 minutes for the whole set.
The problem arise with a sample size of 500, increasing single function
computation time normally, but not the whole process !?
At 11:37 28/05/03, you wrote:
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
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