Displaying 6 results from an estimated 6 matches for "n_gc".
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2018 Jun 08
4
Subsetting the "ROW"s of an object
...neration 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,? 7.4?s 10.9?s 10.66?s 1.22ms 2
> #> 2 arr[i, , , ] 7.06?s 8.8?s 7.85?s 538.09?s 2
>
> So not a huge difference, but it's there.
Fun...
2018 Jun 08
3
Subsetting the "ROW"s of an object
> On Jun 8, 2018, at 10:37 AM, Herv? Pag?s <hpages at 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
2018 Jun 08
0
Subsetting the "ROW"s of an object
...;
>> 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,? 7.4?s 10.9?s 10.66?s 1.22ms 2
>> #> 2 arr[i, , , ] 7.06?s 8.8?s 7.85?s 538.09?s 2
>>
>> So not a huge difference, but it...
2018 Sep 21
1
Bias in R's random integers?
...ot so good since we often have
to get a second random number:
> bench::mark(orig = sample.int(m, 1000000, replace = TRUE),
+ new = sample_int(m, 1000000, replace = TRUE),
+ check = FALSE)
# A tibble: 2 x 14
expression min mean median max `itr/sec` mem_alloc n_gc n_itr
<chr> <bch:t> <bch:t> <bch:t> <bch> <dbl> <bch:byt> <dbl> <int>
1 orig 8.15ms 8.67ms 8.43ms 10ms 115. 3.82MB 4 52
2 new 25.21ms 25.58ms 25.45ms 27ms 39.1 3.82MB 2 18
# ... with 5 m...
2018 Jun 08
0
Subsetting the "ROW"s of an object
...sion 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,? 7.4?s 10.9?s 10.66?s 1.22ms 2
#> 2 arr[i, , , ] 7.06?s 8.8?s 7.85?s 538.09?s 2
So not a huge difference, but it's there.
Hadley
--
http://hadley.nz
2018 Sep 20
5
Bias in R's random integers?
On 9/20/18 1:43 AM, Carl Boettiger wrote:
> For a well-tested C algorithm, based on my reading of Lemire, the unbiased
> "algorithm 3" in https://arxiv.org/abs/1805.10941 is part already of the C
> standard library in OpenBSD and macOS (as arc4random_uniform), and in the
> GNU standard library. Lemire also provides C++ code in the appendix of his
> piece for both this and