Roland Rau wrote:
On 2/16/07, Petr Pikal <petr.pikal at precheza.cz> wrote:
Hi
slight modification of your function can be probably even quicker:
fff<-function(x) sum(2^(which(rev(x))-1))
:-)
Petr
Yes, your function is slightly but consistently faster than my suggestion.
But my "tests" show still Bert Gunter's function to be far ahead of the
rest.
Mere trifling at this point, but here's a tweak that yields slightly
faster performance on my system (with a caveat):
# Bert's original function
bert.gunter <- function(x) {
sum(x * 2^(rev(seq_along(x)) - 1))
}
# A slightly modified function
dead.horse <- function(x) {
sum( 2^(rev(seq_along(x))-1)[x] )
}
set.seed(1)
huge.list <- replicate(20000,
sample(c(TRUE,FALSE), 20, replace=TRUE), simplify=FALSE)
horse.time <- replicate(15, system.time(lapply(huge.list, dead.horse)))
bert.time <- replicate(15, system.time(lapply(huge.list, bert.gunter)))
# Print mean times (exclude first 2 to improve consistency)
rowMeans(bert.time[, -(1:2)])
[1] 0.618600 0.000867 0.621000 0.000000 0.000000
rowMeans(horse.time[, -(1:2)])
[1] 0.580286 0.000571 0.582143 0.000000 0.000000
Hope no one comes along to beat this function ;-)
Incidentally, I generated huge.list by randomly sampling TRUE and FALSE
values with equal probability. I believe this matches what Roland did,
and it seems quite reasonable given that the vectors are meant to
represent binary numbers. But FWIW, as the vectors get more densely
populated with TRUE values, dead.horse() loses its advantage. In the
limit, if all values are TRUE, Bert's multiplication is slightly faster
than logical indexing.
Fun on a Friday...
Cheers,
Jim
10 messages in org.r-project.r-help[R] convert to binary to decimal
