I found the article the "Y of R" in the latest R news to be very
interesting. It is certainly challenging me to learn more about how R works
"under the hood" as the author states. What is less clear to me is whether
this approach is primarily for teaching purposes or has a real world
application. What is meant by "fragility of reliance on the function
name defined as a global variable" as a downside to the classical recursive
formulation of function "s"? How can that impact the average R programmer?
Beyond that, empiricist that I am, I decided to put the examples to the
test. My source code and output is below, but the bottom line consists of 2
observations:
- The Y function approach using csum is consistently slower on my machine
that the s function approach
- The Y function using csum gives recursive error with high input values
just like the s function does
- The Y function in fact reaches the limit of recursion BEFORE the s
function does
Given that it is slower, is more cumbersome to write, and has a lower
nesting limit than the classical approach, I wonder about its utility for
the average programmer (or somewhat below average programmer like me).
Okay, here's my code, output, and sessionInfo()
s <- function(n) {
if (n == 1) return(1)
return(s(n-1)+n)
}
Y <- function(f) {
g <- function(h) function(x) f(h(h))(x)
g(g)
}
csum <- function(f) function(n) {
if (n < 2) return(1);
return(n+f(n-1))
}
recurs.time <- matrix(0, ncol = 3, nrow = 100)
Y.time <- matrix(0, ncol = 3, nrow = 100)
for (i in 1:100) recurs.time[i,] <- unclass(system.time(a <- s(996)))[1:3]
ave.recurs.time <- colSums(recurs.time)
ave.recurs.time
for (i in 1:100) Y.time[i,] <- unclass(system.time(b <- Y (csum)(996)))[1:3]
ave.Y.time <- colSums(Y.time)
ave.Y.time
u <- s(1000)
u
v <- Y (csum)(1000)
v
sessionInfo()
> s <- function(n) {
+ if (n == 1) return(1)
+ return(s(n-1)+n)
+ }
>
>
> Y <- function(f) {
+ g <- function(h) function(x) f(h(h))(x)
+ g(g)
+ }
>
> csum <- function(f) function(n) {
+ if (n < 2) return(1);
+ return(n+f(n-1))
+ }
>
> recurs.time <- matrix(0, ncol = 3, nrow = 100)
> Y.time <- matrix(0, ncol = 3, nrow = 100)
>
> for (i in 1:100) recurs.time[i,] <- unclass(system.time(a <- s(996)))[1:3]
> ave.recurs.time <- colSums(recurs.time)
> ave.recurs.time
[1] 0.356 0.004 0.355
>
> for (i in 1:100) Y.time[i,] <- unclass(system.time(b <- Y
(csum)(996)))[1:3]
> ave.Y.time <- colSums(Y.time)
> ave.Y.time
[1] 0.652 0.000 0.640
>
> u <- s(1000)
> u
[1] 500500
> v <- Y (csum)(1000)
Error: evaluation nested too deeply: infinite recursion /
options(expressions=)?
Error during wrapup: evaluation nested too deeply: infinite recursion /
options(expressions=)?
> v
Error: object "v" not found
No suitable frames for recover()
------------------------------------------------------------
Mark W. Kimpel MD ** Neuroinformatics ** Dept. of Psychiatry
Indiana University School of Medicine
15032 Hunter Court, Westfield, IN 46074
(317) 490-5129 Work, & Mobile & VoiceMail
(317) 399-1219 Home
Skype: mkimpel
"The real problem is not whether machines think but whether men do." -- B.
F. Skinner
******************************************************************
[[alternative HTML version deleted]]
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
