search for: 2e6

Hi all, when I make a polygon with 100,000 vertices my X-server is being killed. This occurs in R-2.9.0 and a freshly installed R-2.10.0 I'm running Ubuntu with a locally compiled R: uname -a Linux onyx 2.6.24-24-generic #1 SMP Tue Aug 18 16:22:17 UTC 2009 x86_64 GNU/Linux xlower = -2e6:2e6 xupper = rev(xlower) ylower = runif(length(xlower)) yupper = ylower+.1 plot(NA,xlim=range(xlower),ylim=range(ylower)) idx=1:10000 # it draws fine for lower number of vertices: polygon(x=c(xlower[idx],xupper[idx]),y=c(ylower[idx],yupper[idx]),col='grey') # but X is killed when I draw 100...

...clear sign of a problem (in my view) > chances are not high that anything will change, unless someone > provides a (small footprint) patch towards the (R-devel aka > "trunk") sources *and* reproducible R code that depicts the > problem. How to reproduce it: a <- replicate(2e6, new.env()) # ~ 1.4 GB of memory rm(a) gc() # the R process still has the memory assigned I?aki

Inline. Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Wed, Oct 14, 2020 at 3:23 PM 1/k^c <kchamberln at gmail.com> wrote: Is which() invoking c-level code by chance, making it slightly faster > on average? > You do not need

...83.163.218.221) when I make a polygon with 100,000 vertices my X-server is being killed. This occurs in R-2.9.0 and a freshly installed R-2.10.0 I'm running Ubuntu with a locally compiled R: uname -a Linux onyx 2.6.24-24-generic #1 SMP Tue Aug 18 16:22:17 UTC 2009 x86_64 GNU/Linux xlower = -2e6:2e6 xupper = rev(xlower) ylower = runif(length(xlower)) yupper = ylower+.1 plot(NA,xlim=range(xlower),ylim=range(ylower)) idx=1:10000 # it draws fine for lower number of vertices: polygon(x=c(xlower[idx],xupper[idx]),y=c(ylower[idx],yupper[idx]),col='grey') # but X is killed when I draw 100...

...up. I have been using the non-xen kernel ever since. My system has both kernel-devel and kernel-xen- devel installed. Just yesterday - I installed the latest Kernels ( kernel 2.6.18-53.1.6.el5 <https://192.168.0.201:10000/software/edit_pack.cgi?search=kernel&package=kernel&version=2%2E6%2E18%2D53%2E1%2E6%2Eel5>) - using gnome software updater which installs both xen and non-xen kernels. The non-xen kernel worked fine without any manual driver install. Presumably the dkms install worked fine. When I tried to boot the xen kernel - there were lots of problems - partly because...

Hi, We've encountered a difference in running time between a straight function call and the same call using do.call when the called function generated an error. We've isolated the problem to the following small reproducible example: Consider the following function: foo <- function(nr = 2e6, nc=3, use.do.call = FALSE) { nn <- paste("V", 1:nc, sep="") z <- data.frame(matrix(rnorm(nr*nc), nrow=nr, ncol = nc, dimnames = list(NULL, nn))) foo2 <- function(x) x[,"V1"] + x[,"V0"] if (use.do.call) do.call(foo2, list(z)) else...

...9.8, command table version 2.0 tcgetattr(/dev/hiddev0): Invalid argument Driver failed to start (exit status=1) Thanks in advance if someone can help ! -- Guillaume Vachon (RHCE, CLP10, CTT+, CNI) Conseiller en TI, Formateur Infoglobe, http://infoglobe.ca 1963, Frank-Carrel, 201 - Qu?bec, QC G1N 2E6 T?l?phone : 418.681.2929 #123

...ile("data.txt","r") child <- foreach(i = icount(40)) %dopar% { scan(file,what = "character",sep = "\n",skip = 0,nlines = 1e6) } Thus, each child would have a different skip argument. child[[1]]: skip = 0, child[[2]]: skip = 1e6 + 1, child[[3]]: skip = 2e6 + 1, ... ,child[[40]]: skip = 39e6 + 1. I would then end up with a list of 40 vectors with child[[1]] containing records 1 to 1000000, child[[2]] containing records 1000001 to 2000000, ... ,child[[40]] containing records 39000001 to 40000000. Also, would one file connection suffice or does their...

I'm reading a file and using the file to populate a data frame. The way the file is laid out, I need to fill in the data frame one row at a time. When I start reading my file, I don't know how many rows I will need. It's on the order of a million. Being mindful of the time expense of reallocation, I decided on a strategy of doubling the data frame size every time I needed to expand

...at anything will change, unless someone > >> provides a (small footprint) patch towards the (R-devel aka > >> "trunk") sources *and* reproducible R code that depicts the > >> problem. > > > > How to reproduce it: > > > > a <- replicate(2e6, new.env()) # ~ 1.4 GB of memory > > rm(a) > > gc() # the R process still has the memory assigned > > > > Right, but that's unavoidable because of the way Linux allocates memory - > see FAQ 7.42 > The memory is free, Linux just keeps it for future allocations. >...

...>> chances are not high that anything will change, unless someone >> provides a (small footprint) patch towards the (R-devel aka >> "trunk") sources *and* reproducible R code that depicts the >> problem. > > How to reproduce it: > > a <- replicate(2e6, new.env()) # ~ 1.4 GB of memory > rm(a) > gc() # the R process still has the memory assigned > Right, but that's unavoidable because of the way Linux allocates memory - see FAQ 7.42 The memory is free, Linux just keeps it for future allocations. Running malloc.trim doesn't help...

...it. Alternatively, I can redefine one element as double which then redefines them all. Both methods are quick, so I guess I shouldn't complain, but I would have thought there'd be something more obvious. Have I missed it? Here's my redimensioning example: ## Matrix M... M <- 1:2e6 ; dim(Mi) <- c(1e3,2e3) dim(M) class(M) ## ...has integer elements, e.g., class(M[1,1]) ## The as.double() command changes ## these to double-precision, but it ## also strips away dimensions... Md <- as.double(Mi) dim(Md) class(Md) ## ...so I have to put them back. dim(Md) <- dim(Mi) dim(...

...ouldn't find it. Could you explain why it is using the value inside of the sqrt (1000/(pi * lambda)).   Many thanks in advance if you do have the time to answer,   Myriam Saavedra   Universidad San Francisco de Quito ....................... Adress Home 3808 Lillooet St. VANCOUVER BC CANADA, V5R-2E6 [[alternative HTML version deleted]]

..., format() for object_size objects (returned by object.size()) uses Kb, Mb and Gb, which AFAIK is more often used to indicate sizes in kilobits, megabits and gigabits, e.g. > format(structure(2e3, class="object_size"), units="auto") [1] "2 Kb" > format(structure(2e6, class="object_size"), units="auto") [1] "1.9 Mb" > format(structure(2e9, class="object_size"), units="auto") [1] "1.9 Gb" > format(structure(2e12, class="object_size"), units="auto") [1] "1862.6 Gb" BT...

...rstand. Once I create a 2.1.x tree it will be included. For those of you on the mailing list wondering what the patch is (listar eats attachments), I've made it available at http://lly.org/~rcw/abcde/misc/abcde-flac.tar.gz. -- Robert Woodcock - rcw@debian.org double a,b=4,c;main(){for(;++a<2e6;c-=(b=-b)/a++);printf("%f\n",c);} ----- End forwarded message ----- -- - mdz

Hello, I have an error message that doesn't seem to make sense in that having read the R documentation I was under the impression that R was able to grab as much memory as it needed, and has been able to do so for some time, so the advice given below about increasing the size of the workspace is redundant. If I'm right, does anyone have a solution to the problem of the size of

Hello everyone, I'm writting a little script that will read a matrix from a file i.e. 0,.11,.22,.4 .11,0,.5,.3 .22,.5,0,.7 anb so on and will then calculate some standard stats for nets (i.e. centralization, degree, etc). So far I have opened the file and read the contents, however I' m using readLines(filename) to read the file and it returns it as

Hi Dr. Snow, & R-helpers, Thank you for your reply! I hadn't heard of the {microbenchmark} package & was excited to try it! Thank you for the suggestion! I did check the reference source for which() beforehand, which included the statement to remove NAa, and I didn't have any missing values or NAs: sum(is.na(dat$gender2)) sum(is.na(dat$gender)) sum(is.na(dat$y)) [1] 0 [1] 0 [1]

...ess someone >> >> provides a (small footprint) patch towards the (R-devel aka >> >> "trunk") sources *and* reproducible R code that depicts the >> >> problem. >> > >> > How to reproduce it: >> > >> > a <- replicate(2e6, new.env()) # ~ 1.4 GB of memory >> > rm(a) >> > gc() # the R process still has the memory assigned >> > >> >> Right, but that's unavoidable because of the way Linux allocates memory - >> see FAQ 7.42 >> The memory is free, Linux just keeps it...

Hi mailing list and R-core. Could someone from R-core please help me to create account in bugzilla? I would like to submit issue related to gc() to wishlist. Related context is here - https://stat.ethz.ch/pipermail/r-devel/2017-July/074715.html -- Regards Dmitriy Selivanov [[alternative HTML version deleted]]