search for: 1000s

Hi all, I have to transfer thousands of files across my network. We are looking at using rsync via a squid proxy to help improve the network tuning. What I am witnessing now is that with each successive file, the transfer speed resets to 0 and ramps up to 2.5MB/s. Is there anyway that I can group these transfers together to take better advantage of our bandwidth? I should be able to peak at

I recently got a tegra TK1 and was curious how fast it was compared to my previous arm "build machine": the original arm Samsung chromebook. I timed running ninja to build just llvm in Release+Asserts using clang as the host compiler. chromebook: real 84m30.939s user 163m50.145s sys 4m0.100s TK1: real 34m7.376s user 132m44.417s sys 3m3.543s A really nice

My UPS is a Eaton Ellipse 1000S (But its identified as MGE Ellipse 0463:ffff). I have been using the usb interface with the usbhid-ups driver, but I found that sometimes (too much times) the driver doesn't respond and NUT doesn't receive changes nor notifications from the UPS. The kernel lose the connections with USB de...

> I assume there are lots of backwards-compatibility issues as well as valid > use cases for this behavior, so I guess defaulting to M[1:6, 1:6] is out of > the question. Agree. > Is there any scope for adding a new argument to head.matrix that would > allow this flexibility? I agree with what you're trying to achieve. However, I'm not sure this is as simple as you're

Hi, This code loops through my results and saves them : @results.each{|r| r.save} There are potentially thousands of results. This takes ages to complete. Is there a quicker way to do this? Any help is greately appreciated Thanks Chris -- Posted via http://www.ruby-forum.com/.

I have a gene expression matrix n (genes) X p (cases), where n = 8000 and p = 100. I want to fit each gene as univariate in a coxph model, i.e., fitting 8000 models. I do something like this: res <- apply(data, 1, coxph.func) which takes about 4 min, not bad. But I need to do large numbers of permutations of the data (permuting the columns), for example, 2000, which would take 5 days. I would

I have a small pile of them from customers who were too lazy to send them back after switching to our local voice service... Is there any hope of ever using these things with Asterisk? Vonage does not want them back and they won't unlock them either. A terrible shame! Should I just toss them? Steve

>>>>> Gabriel Becker >>>>> on Tue, 29 Oct 2019 12:43:15 -0700 writes: > Hi all, > So I've started working on this and I ran into something that I didn't > know, namely that for x a multi-dimensional (2+) array, head(x) and tail(x) > ignore dimension completely, treat x as an atomic vector, and return an > (unclassed)

...he png files. I want to create 1000 png files, starting with the first stock path, then the second and so on. So I start with the first path, add a second to the plot, add the third and so on, so at the end I have a plot with 1000 simulations, here is my code: for(i in 1:1000){ #jpeg(paste("1000s",i,".png",sep="")) plot(c(1:23),matrix[,1],type="l",ylim=c(17,24)) lines(c(1:23),matrix[,i],type="l",col=i) #dev.off() } Here is the problem, that each additional part disappears when the loop gets to the next value, so I tried: plot(0,0 , xlim=c(1,23)...

Finally read in detail your response Gabe. Looks great, and I agree it's quite intuitive, as well as agree against non-recycling. Once the length(n) == length(dim(x)) behavior is enabled, I don't think there's any need/desire to have head() do x[1:6,1:6] anymore. head(x, c(6, 6)) is quite clear for those familiar with head(x, 6), it would seem to me. Mike C On Sat, Jul 13, 2019 at

>>>>> peter dalgaard >>>>> on Thu, 31 Oct 2019 23:04:29 +0100 writes: > Hmm, the problem I see here is that these implied classes are all inherently one-off. We also have >> inherits(matrix(1,1,1),"numeric") > [1] FALSE >> is.numeric(matrix(1,1,1)) > [1] TRUE >> inherits(1L,"numeric")

Awesome. Gabe, since you already have a workshopped version, would you like to proceed? Feel free to ping me to review the patch once it's posted. On Mon, Sep 16, 2019 at 3:26 PM Martin Maechler <maechler at stat.math.ethz.ch> wrote: > >>>>> Michael Chirico > >>>>> on Sun, 15 Sep 2019 20:52:34 +0800 writes: > > > Finally read in

I think of head() as a standard helper for "glancing" at objects, so I'm sometimes surprised that head() produces massive output: M = matrix(nrow = 10L, ncol = 100000L) print(head(M)) # <- beware, could be a huge print I assume there are lots of backwards-compatibility issues as well as valid use cases for this behavior, so I guess defaulting to M[1:6, 1:6] is out of the

...heir userland from 6.3 to 6.8 but only selected few packages. The picture seems to be that their company runs a lot of apps on 6.3 userland and might have some specific dependencies, etc., but more importantly, this environment has been running in customers' environment for quite some time esp 1000s of customers, so updating system properly is not easily feasible for this scenario. However, they can hand pick packages seem fit for update that can be pushed out using their internal code fixes and updates for end users. SO, this seems to be the problem of trying to hand pick certain packages to...

As I hit send I realized I did know of one, which is table objects. So while we're discussing it we can talk about both generally and specifically what head.table and tail.table should do. Looks like tail.table is already special -cased to hit the matrix method if it is 2d, so the natural extension of that would be hitting tail.array for any 2+d table, I think. ~G On Sat, Nov 2, 2019 at

Dear Herve, The brief() generic function in the car package does something very similar to that for data frames (and has methods for other classes of objects as well). Best, John ----------------------------- John Fox, Professor Emeritus McMaster University Hamilton, Ontario, Canada Web: http::/socserv.mcmaster.ca/jfox > On Sep 17, 2019, at 2:52 AM, Pages, Herve <hpages at

Hello, I am wondering if there are any possibilities to save HDD space used by emails by compressing content (not just attachements). It would be many 1000s email boxes and probably each using lot of space so would be easier if we can just compress emails somehow. Thanks, Cristian -------------- next part -------------- An HTML attachment was scrubbed... URL: <http://dovecot.org/pipermail/dovecot/attachments/20070214/176bff3d/attachment-0001.html&...

Dear Herve, Sorry, I should have said "matrices" rather than "data frames" -- brief() has methods for both. Best, John ----------------------------- John Fox, Professor Emeritus McMaster University Hamilton, Ontario, Canada Web: http::/socserv.mcmaster.ca/jfox > On Sep 17, 2019, at 8:29 AM, Fox, John <jfox at mcmaster.ca> wrote: > > Dear Herve,

Hi all, I have made a loop function which prints outputs. For example:  for (i in 1:5) + print(i) [1] 1 [1] 2 [1] 3 [1] 4 [1] 5 However, I would like the output to given as a list or a vector, e..g c(1,2,3,4,5). Is it possible to do this? (The function I am using outputs many 1000s of results, so is not easy to change to a vector manually.) Many Thanks. [[alternative HTML version deleted]]

...nvelope_sender, envelope_recipient, header_sender, header_recipient canonical_maps = cleanup_service_name = cleanup command_directory = /usr/sbin command_execution_directory = command_expansion_filter = 1234567890!@%-_=+:,./abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ command_time_limit = 1000s config_directory = /etc/postfix connection_cache_protocol_timeout = 5s connection_cache_service_name = scache connection_cache_status_update_time = 600s connection_cache_ttl_limit = 2s content_filter = daemon_directory = /usr/lib/postfix daemon_timeout = 18000s debug_peer_level = 2 debug_peer_list...