I think you answered your own question. For loops are not a boogeyman... poor
memory management is.
Algorithms that are sensitive to evaluation sequence are often not very
re-usable, and certainly not parallelizable. If you have a specific algorithm
in mind, there may be some advice we can give
You are mistaken. apply() is *not* vectorized. It is a disguised loop.
For true vectorization at the C level, the answer must be no, as the
whole point is to treat the argument as a whole object and hide the
iterative details.
However, as you indicated, you can always manually randomize the
index
nBuyMat <- data.frame(matrix(rnorm(28), 7, 4))
nBuyMat
nBuy <- nrow(nBuyMat)
sample(1:nBuy, nBuy, replace=FALSE)
sample(1:nBuy)
sample(nBuy)
?sample
apply(nBuyMat[sample(1:nBuy,nBuy, replace=FALSE),], 1, function(x) sum(x))
apply(nBuyMat[sample(nBuy),], 1, function(x) sum(x))
The defaults for sa
Is there a way to use vectorization where the elements are evaluated in a
random order?
For instance, if the code is to be run on each row in a matrix of length nBuy
the following will do the job
for (b in sample(1:nBuy,nBuy, replace=FALSE)){
}
but
apply(nBuyMat, 1, function(x))
will be run
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