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.
Hadley
--
http://hadley.nz
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