I have the following code:
--8<---------------cut here---------------start------------->8---
d <- rep(10,10)
for (i in 1:100) {
a <- sample.int(length(d), size = 2)
if (d[a[1]] >= 1) {
d[a[1]] <- d[a[1]] - 1
d[a[2]] <- d[a[2]] + 1
}
}
--8<---------------cut here---------------end--------------->8---
it does what I want, i.e., modified vector d 100 times.
Now, if I want to repeat this 1e6 times instead of 1e2 times, I want to
vectorize it for speed, so I do this:
--8<---------------cut here---------------start------------->8---
update <- function (i) {
a <- sample.int(n.agents, size = 2)
if (d[a[1]] >= delta) {
d[a[1]] <- d[a[1]] - 1
d[a[2]] <- d[a[2]] + 1
}
entropy(d, unit="log2")
}
system.time(entropy.history <- sapply(1:1e6,update))
--8<---------------cut here---------------end--------------->8---
however, the global d is not modified, apparently update modifies the
local copy.
so,
1. is there a way for a function to modify a global variable?
2. how would you vectorize this loop?
thanks!
--
Sam Steingold (http://sds.podval.org/) on Ubuntu 12.04 (precise) X 11.0.11103000
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A number problem "solved" with floats turns into 1.9999999999999998 problems.
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