Menu
The fastmatch package implements a much faster version of match for repeated matches (e.g. in a loop):
set.seed(1) library(fastmatch) table <- 1L:100000L x <- sample(table, 10000, replace=TRUE) system.time(for(i in 1:100) a <- match(x, table)) system.time(for(i in 1:100) b <- fmatch(x, table)) identical(a, b) Is there a similar implementation for %in% I could use to speed up repeated lookups?
597
Are operators faster than functions? Calling a function at runtime is potentially slower than not calling a function. But, as we've found out, an operator can actually internally call a function. Besides, a function call for the abstract machine doesn't necessarily mean that a function will be called at runtime.
Some processors are quicker when comparing against zero. So > 0 might be faster than >= 1 , as an example. @Simple A decent compiler will replace >= 1 with > 0 if it's faster.
Is the in operator's speed in Python proportional to the length of the iterable? Yes. The time for in to run on a list of length n is O(n) . It should be noted that it is O(1) for x in set and x in dict as they are hashed, and in is constant time.
Look at the definition of %in%:
R> `%in%` function (x, table) match(x, table, nomatch = 0L) > 0L <bytecode: 0x1fab7a8> <environment: namespace:base> It's easy to write your own %fin% function:
`%fin%` <- function(x, table) { stopifnot(require(fastmatch)) fmatch(x, table, nomatch = 0L) > 0L } system.time(for(i in 1:100) a <- x %in% table) # user system elapsed # 1.780 0.000 1.782 system.time(for(i in 1:100) b <- x %fin% table) # user system elapsed # 0.052 0.000 0.054 identical(a, b) # [1] TRUE If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With
