The C code for subsetting doesn't need to recycle a logical subscript.
It only needs to walk on it and start again at the beginning of the
vector when it reaches the end. Not exactly the same as detecting the
"take everything along that dimension" situation though.
x[TRUE, TRUE, TRUE] triggers the full subsetting machinery when x[]
and x[ , , ] could (and should) easily avoid it.
H.
On 06/08/2018 01:49 PM, Hadley Wickham wrote:
Hmmm, yes, there must be some special case in the C code to avoid
recycling a length-1 logical vector:
dims <- c(4, 4, 4, 1e5)
arr <- array(rnorm(prod(dims)), dims)
dim(arr)
#> [1] 4 4 4 100000
i <- c(1, 3)
bench::mark(
arr[i, TRUE, TRUE, TRUE],
arr[i, , , ]
)[c("expression", "min", "mean", "max")]
#> # A tibble: 2 x 4
#> expression min mean max
#> <chr> <bch:tm> <bch:tm> <bch:tm>
#> 1 arr[i, TRUE, TRUE, TRUE] 41.8ms 43.6ms 46.5ms
#> 2 arr[i, , , ] 41.7ms 43.1ms 46.3ms
On Fri, Jun 8, 2018 at 12:31 PM, Berry, Charles <ccbe...@ucsd.edu> wrote:
On Jun 8, 2018, at 11:52 AM, Hadley Wickham <h.wick...@gmail.com> wrote:
On Fri, Jun 8, 2018 at 11:38 AM, Berry, Charles <ccbe...@ucsd.edu> wrote:
On Jun 8, 2018, at 10:37 AM, Hervé Pagès <hpa...@fredhutch.org> wrote:
Also the TRUEs cause problems if some dimensions are 0:
matrix(raw(0), nrow=5, ncol=0)[1:3 , TRUE]
Error in matrix(raw(0), nrow = 5, ncol = 0)[1:3, TRUE] :
(subscript) logical subscript too long
OK. But this is easy enough to handle.
H.
On 06/08/2018 10:29 AM, Hadley Wickham wrote:
I suspect this will have suboptimal performance since the TRUEs will
get recycled. (Maybe there is, or could be, ALTREP, support for
recycling)
Hadley
AFAICS, it is not an issue. Taking
arr <- array(rnorm(2^22),c(2^10,4,4,4))
as a test case
and using a function that will either use the literal code `x[i,,,,drop=FALSE]'
or `eval(mc)':
subset_ROW4 <-
function(x, i, useLiteral=FALSE)
{
literal <- quote(x[i,,,,drop=FALSE])
mc <- quote(x[i])
nd <- max(1L, length(dim(x)))
mc[seq(4,length=nd-1L)] <- rep(TRUE, nd-1L)
mc[["drop"]] <- FALSE
if (useLiteral)
eval(literal)
else
eval(mc)
}
I get identical times with
system.time(for (i in 1:10000) subset_ROW4(arr,seq(1,length=10,by=100),TRUE))
and with
system.time(for (i in 1:10000) subset_ROW4(arr,seq(1,length=10,by=100),FALSE))
I think that's because you used a relatively low precision timing
mechnaism, and included the index generation in the timing. I see:
arr <- array(rnorm(2^22),c(2^10,4,4,4))
i <- seq(1,length = 10, by = 100)
bench::mark(
arr[i, TRUE, TRUE, TRUE],
arr[i, , , ]
)
#> # A tibble: 2 x 1
#> expression min mean median max n_gc
#> <chr> <bch:t> <bch:t> <bch:tm> <bch:tm> <dbl>
#> 1 arr[i, TRUE,… 7.4µs 10.9µs 10.66µs 1.22ms 2
#> 2 arr[i, , , ] 7.06µs 8.8µs 7.85µs 538.09µs 2
So not a huge difference, but it's there.
Funny. I get similar results to yours above albeit with smaller differences.
Usually < 5 percent.
But with subset_ROW4 I see no consistent difference.
In this example, it runs faster on average using `eval(mc)' to return the
result:
arr <- array(rnorm(2^22),c(2^10,4,4,4))
i <- seq(1,length=10,by=100)
bench::mark(subset_ROW4(arr,i,FALSE), subset_ROW4(arr,i,TRUE))[,1:8]
# A tibble: 2 x 8
expression min mean median max `itr/sec`
mem_alloc n_gc
<chr> <bch:tm> <bch:tm> <bch:tm> <bch:tm> <dbl> <bch:byt>
<dbl>
1 subset_ROW4(arr, i, FALSE) 28.9µs 34.9µs 32.1µs 1.36ms 28686.
5.05KB 5
2 subset_ROW4(arr, i, TRUE) 28.9µs 35µs 32.4µs 875.11µs 28572.
5.05KB 5
And on subsequent reps the lead switches back and forth.
Chuck
--
Hervé Pagès
Program in Computational Biology
Division of Public Health Sciences
Fred Hutchinson Cancer Research Center
1100 Fairview Ave. N, M1-B514
P.O. Box 19024
Seattle, WA 98109-1024
E-mail: hpa...@fredhutch.org
Phone: (206) 667-5791
Fax: (206) 667-1319
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