Displaying 20 results from an estimated 94 matches for "neval".
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2013 Jul 02
2
cache most-recent dispatch
...lt;- selectMethod(paste, class(x[[1]]))
sapply(x, paste, collapse="+")
}
lst <- split(rep(LETTERS, 100), rep(1:1300, 2))
library(microbenchmark)
microbenchmark(fun0(lst), times=10)
## Unit: milliseconds
## expr min lq median uq max neval
## fun0(lst) 4.153287 4.180659 4.513539 5.19261 5.280481 10
setGeneric("paste")
microbenchmark(fun0(lst), fun1(lst), times=10)
## > microbenchmark(fun0(lst), fun1(lst), times=10)
## Unit: milliseconds
## expr min lq median uq...
2015 Jan 26
2
speedbump in library
...amespaces()
f2 <- function(pkg) !is.null(.getNamespace(pkg))
require(microbenchmark)
pkg <- "foo"; (mbM <- microbenchmark(r1 <- f1(pkg), r2 <- f2(pkg))); stopifnot(identical(r1,r2)); r1
## Unit: microseconds
## expr min lq mean median uq max neval cld
## r1 <- f1(pkg) 38.516 40.9790 42.35037 41.7245 42.4060 82.922 100 b
## r2 <- f2(pkg) 1.331 1.8285 2.13874 2.0855 2.3365 7.252 100 a
## [1] FALSE
pkg <- "stats"; (mbM <- microbenchmark(r1 <- f1(pkg), r2 <- f2(pkg))); stopifnot(identical(r1,r2)); r1
#...
2017 Aug 04
2
Why is as.function() slower than eval(call("function"())?
...function(c(a, list(b)), env = parent.frame())
a <- as.pairlist(alist(x = , y = ))
b <- quote(x + y)
library("microbenchmark")
microbenchmark(make_fn_1(a, b), make_fn_2(a, b))
# Unit: microseconds
# expr min lq mean median uq max neval cld
# make_fn_1(a, b) 1.671 1.8855 2.13297 2.039 2.1950 9.852 100 a
# make_fn_2(a, b) 3.541 3.7230 4.13400 3.906 4.1055 23.153 100 b
At first I thought the gap was due to the overhead of calling c(a, list(b)). But this turns out not to be the case:
make_fn_weird <- func...
2018 Mar 13
2
Possible Improvement to sapply
FYI, in R devel (to become 3.5.0), there's isFALSE() which will cut
some corners compared to identical():
> microbenchmark::microbenchmark(identical(FALSE, FALSE), isFALSE(FALSE))
Unit: nanoseconds
expr min lq mean median uq max neval
identical(FALSE, FALSE) 984 1138 1694.13 1218.0 1337.5 13584 100
isFALSE(FALSE) 713 761 1133.53 809.5 871.5 18619 100
> microbenchmark::microbenchmark(identical(TRUE, FALSE), isFALSE(TRUE))
Unit: nanoseconds
expr min lq mean median uq max neval...
2018 Feb 11
4
Parallel assignments and goto
...itude slower than the first version.
> microbenchmark::microbenchmark(factorial(100),
+ factorial_tr_1(100),
+ factorial_tr_2(100))
Unit: microseconds
expr min lq mean median uq max neval
factorial(100) 53.978 60.543 77.76203 71.0635 85.947 180.251 100
factorial_tr_1(100) 9.022 9.903 11.52563 11.0430 11.984 28.464 100
factorial_tr_2(100) 5870.565 6109.905 6534.13607 6320.4830 6756.463 8177.635 100
Is there another way to do parallel assignmen...
2018 Feb 27
2
Parallel assignments and goto
...ial(1000),
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?factorial_tr_manual(1000),
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?factorial_tr_automatic_1(1000),
? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ?factorial_tr_automatic_2(1000))
Unit: microseconds
? ? ? ? ? ? ? ? ? ? ? ? ? ?expr ? ? min ? ? ?lq ? ? ?mean ? median ? ? ? ?uq ? ? ?max neval
? ? ? ? ? ? ? ? factorial(1000) 884.137 942.060 1076.3949 977.6235 1042.5035 2889.779 ? 100
? ? ? factorial_tr_manual(1000) 110.215 116.919 ?130.2337 118.7350 ?122.7495 ?255.062 ? 100
?factorial_tr_automatic_1(1000) 179.897 183.437 ?212.8879 187.8250 ?195.7670 ?979.352 ? 100
?factorial_tr_automatic...
2017 Nov 20
2
Small performance bug in [.Date
Hi all,
I think there's an unnecessary line in [.Date which has a considerable
impact on performance when subsetting large dates:
x <- Sys.Date() + 1:1e6
microbenchmark::microbenchmark(x[1])
#> Unit: microseconds
#> expr min lq mean median uq max neval
#> x[1] 920.651 1039.346 3624.833 2294.404 3786.881 41176.38 100
`[.Date` <- function(x, ..., drop = TRUE) {
cl <- oldClass(x)
# class(x) <- NULL
val <- NextMethod("[")
class(val) <- cl
val
}
microbenchmark::microbenchmark(x[1])
#> Unit: micros...
2018 Mar 13
4
Possible Improvement to sapply
...nswer) <- X
if (simplify && length(answer))
simplify2array(answer, higher = (simplify == "array"))
else answer
}
> microbenchmark(sapply(myList, length), times = 10000L)
Unit: microseconds
expr min lq mean median uq max neval
sapply(myList, length) 14.156 15.572 16.67603 15.926 16.634 650.46 10000
> microbenchmark(mySapply(myList, length), times = 10000L)
Unit: microseconds
expr min lq mean median uq max neval
mySapply(myList, length) 13.095 14.864 16.02964 15.218 15.573 167...
2018 Feb 26
0
Parallel assignments and goto
...ean ? median
? ? ? ? ? ? ? ? factorial(1000) 756.357 810.4135 963.1040 856.3315
? ? ? factorial_tr_manual(1000) 104.838 119.7595 198.7347 129.0870
?factorial_tr_automatic_1(1000) 112.354 125.5145 211.6148 135.5255
?factorial_tr_automatic_2(1000) 461.015 544.7035 688.5988 565.3240
? ? ? ?uq ? ? ?max neval
?945.3110 4149.099 ? 100
?136.8200 4190.331 ? 100
?152.9625 5944.312 ? 100
?600.5235 7798.622 ? 100
The simple solution, of course, is to not do that, but then I can?t handle expressions inside calls to ?with?. And I would really like to, because then I can combine tail recursion with pattern matc...
2018 Feb 27
0
Parallel assignments and goto
...factorial_tr_manual(1000),
> factorial_tr_automatic_1(1000),
> factorial_tr_automatic_2(1000))
> Unit: microseconds
> expr min lq mean median
> uq max neval
> factorial(1000) 884.137 942.060 1076.3949 977.6235
> 1042.5035 2889.779 100
> factorial_tr_manual(1000) 110.215 116.919 130.2337 118.7350
> 122.7495 255.062 100
> factorial_tr_automatic_1(1000) 179.897 183.437 212.8879 187.8250
> 195.7670 979.352...
2024 Feb 29
2
[External] converting MATLAB -> R | element-wise operation
...w = 2, byrow = TRUE) # Example matrix
lambda <- c(2, 3, 4) # Example vector
colNN <- t(NN)
microbenchmark(
sweep = sweep(NN, 2, lambda, "/"),
transpose = t(t(NN)/lambda),
colNN = colNN/lambda
)
Unit: nanoseconds
expr min lq mean median uq max neval cld
sweep 13817 14145 15115.06 14350 14657.5 75932 100 a
transpose 1845 1927 2151.68 2132 2214.0 7093 100 b
colNN 82 123 141.86 123 164.0 492 100 c
Note that transpose is much faster than sweep because it is doing less work,
I believe essentially just c...
2017 Aug 22
4
How to benchmark speed of load/readRDS correctly
...> microbenchmark(
+ n <- readRDS('file.rds'),
+ load('file.Rdata')
+ )
Unit: milliseconds
expr min lq mean median uq max neval
n <- readRDS(fl1) 106.5956 109.6457 237.3844 117.8956 141.9921 10934.162 100
load(fl2) 295.0654 301.8162 335.6266 308.3757 319.6965 1915.706...
2005 May 09
2
use "integrate" for functions defined in C, not R
..._dqags", I define a C function "my_call_dqags" that
has it's own parameters of "lower, upper" and etc define in C, instead of
parsing from R. And I call
Rdqags(Cintfn,
&lower, &upper, &epsabs, &epsrel, &result,
&abserr, &neval, &ier, &limit, &lenw, &last, iwork, work);
instead of
Rdqags(Rintfn, (void*)&is,
&lower, &upper, &epsabs, &epsrel, &result,
&abserr, &neval, &ier, &limit, &lenw, &last, iwork, work);
I am not passing (void*)&a...
2020 Nov 02
3
parallel PSOCK connection latency is greater on Linux?
...DELAY causes many small
packets to be sent, it might make more sense to set TCP_QUICKACK
instead.
I?aki
> Unit: microseconds
> expr min lq mean median uq max
> clusterEvalQ(cl, iris) 1449.997 43991.99 43975.21 43997.1 44001.91 48027.83
> neval
> 1000
>
> exactly the same machine + R but with TCP_NODELAY enabled in R_SockConnect():
>
> Unit: microseconds
> expr min lq mean median uq max neval
> clusterEvalQ(cl, iris) 156.125 166.41 180.8806 170.247 174.298 5322.234 1000
&g...
2018 Mar 14
0
Possible Improvement to sapply
...ites:
> FYI, in R devel (to become 3.5.0), there's isFALSE() which will cut
> some corners compared to identical():
> > microbenchmark::microbenchmark(identical(FALSE, FALSE), isFALSE(FALSE))
> Unit: nanoseconds
> expr min lq mean median uq max neval
> identical(FALSE, FALSE) 984 1138 1694.13 1218.0 1337.5 13584 100
> isFALSE(FALSE) 713 761 1133.53 809.5 871.5 18619 100
> > microbenchmark::microbenchmark(identical(TRUE, FALSE), isFALSE(TRUE))
> Unit: nanoseconds
> expr min lq mean...
2018 Mar 13
1
Possible Improvement to sapply
...fy2array(answer, higher = (simplify == "array"))
> > else answer
> > }
> >
> >
> >> microbenchmark(sapply(myList, length), times = 10000L)
> > Unit: microseconds
> > expr min lq mean median uq max
> neval
> > sapply(myList, length) 14.156 15.572 16.67603 15.926 16.634 650.46
> > 10000
> >> microbenchmark(mySapply(myList, length), times = 10000L)
> > Unit: microseconds
> > expr min lq mean median uq max
> neval
> >...
2004 Nov 18
0
Calling Rdqags doesn't produce correct result.
...m for numerical integration. Following are the C
program
and the corresponding R program:
C program
---------
void test(double *a,
double *b,
double *epsabs,
double *epsrel,
double *result,
double *abserr,
int *neval,
int *ier,
int *limit,
int *lenw,
int *last,
int *iwork,
double *work,
double *exx)
{
void *ex;
ex = exx;
Rdqags(tmpfun, ex, a, b, epsabs, epsrel,
result, abserr, neval...
2015 Jan 26
2
speedbump in library
...tNamespace(pkg))
>>
>> require(microbenchmark)
>>
>> pkg <- "foo"; (mbM <- microbenchmark(r1 <- f1(pkg), r2 <-
>> f2(pkg))); stopifnot(identical(r1,r2)); r1 ## Unit:
>> microseconds ## expr min lq mean median uq max neval cld
>> ## r1 <- f1(pkg) 38.516 40.9790 42.35037 41.7245 42.4060
>> 82.922 100 b ## r2 <- f2(pkg) 1.331 1.8285 2.13874 2.0855
>> 2.3365 7.252 100 a ## [1] FALSE
>>
>> pkg <- "stats"; (mbM <- microbenchmark(r1 <- f1(pkg), r2...
2020 Nov 01
2
parallel PSOCK connection latency is greater on Linux?
I'm exploring latency overhead of parallel PSOCK workers and noticed
that serializing/unserializing data back to the main R session is
significantly slower on Linux than it is on Windows/MacOS with similar
hardware. Is there a reason for this difference and is there a way to
avoid the apparent additional Linux overhead?
I attempted to isolate the behavior with a test that simply returns
2018 Mar 13
0
Possible Improvement to sapply
...mp; length(answer))
> simplify2array(answer, higher = (simplify == "array"))
> else answer
> }
>
>
>> microbenchmark(sapply(myList, length), times = 10000L)
> Unit: microseconds
> expr min lq mean median uq max neval
> sapply(myList, length) 14.156 15.572 16.67603 15.926 16.634 650.46
> 10000
>> microbenchmark(mySapply(myList, length), times = 10000L)
> Unit: microseconds
> expr min lq mean median uq max neval
> mySapply(myList, length) 13.095 1...