search for: 1000l

..., 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L), Total = c(0L, 0L, 326L, 1735L, 1807L, 2208L, 3883L, 8820L, 6060L, 19326L, 63158L, 100718L, 53015L, 91689L, 152629L, 122708L, 61293L, 15574L, 86538L, 75365L, 303259L, 19691L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 161L, 321L, 1000L, 4425L, 13202L, 19726L, 30518L, 84949L, 157260L, 145691L, 85801L, 62044L, 44439L, 23272L, 22391L, 20159L, 14854L, 35379L, 31142L, 7736L, 13221L, 4894L), Fry = c(0L, 0L, 326L, 1735L, 1807L, 2208L, 3883L, 8759L, 6060L, 19326L, 63119L, 100524L, 52582L, 88170L, 145564L, 111416L, 38233L, 5248L, 178...

...CHH 0 0.0 + > dput(data) structure(list(V1 = structure(c(1L, 1L, 3L, 3L, 4L, 5L, 6L, 6L, 7L, 2L), .Label = c("chr1", "chr10", "chr2", "chr3", "chr4", "chr5", "chr7"), class = "factor"), V2 = c(500L, 550L, 700L, 1000L, 100L, 450L, 450L, 50034L, 50055L, 50063L), V3 = c(500L, 550L, 700L, 1000L, 100L, 450L, 450L, 50034L, 50055L, 50063L), V4 = structure(c(3L, 3L, 3L, 3L, 3L, 1L, 3L, 2L, 2L, 3L), .Label = c("CG", "CHG", "CHH"), class = "factor"), V5 = c(0L, 0L, 0L, 0...

...ll night: ----- SNIP ----- Traversing all blocks to verify checksums ... zdb_blkptr_cb: Got error 50 reading <0, 80, 0, 0> DVA[0]=<0:28b61ec9600:1200> DVA[1]=<0:149e50a5400:1200> DVA[2]=<0:9f83efe800:1200> [L0 SPA space map] fletcher4 lzjb LE contiguous unique triple size=1000L/a00P birth=2561650L/2561650P fill=1 cksum=6e4fd979ea:97c2e68bdf84:7cfea1e3255982:4ca2471aa9f3c833 -- skipping zdb_blkptr_cb: Got error 50 reading <0, 80, 0, 1> DVA[0]=<0:28b61ecba00:1200> DVA[1]=<0:149e50a7800:1200> DVA[2]=<0:9f83f00c00:1200> [L0 SPA space map] fletcher4 lzj...

...r a list of 3 vectors of length 5). Unsurprisingly, I think, because the unsplit() approach works for the general case whereas the do.call(rbind) only works for the balanced structure of the toy example, do.call(rbind) took about 1/10th the time of unsplit: > microbenchmark(unsplit(x,f),times = 1000L) Unit: microseconds expr min lq mean median uq max neval unsplit(x, f) 63.058 64.042 70.44419 65.682 67.24 3893.155 1000 -------------- > microbenchmark(as.vector(do.call(rbind,x)),times = 1000L) Unit: microseconds expr min lq mean...

Hello! I am optimizing my code in R and for this I need to know a bit more about the internals. It would help tremendously if someone could link me to a page with O()-complexities of all the operations. In this particular case, I need something like a linked list with O(1) insertLast/First ability. I can't preallocate a vector since I do not know the final size of the list ahead of time. The

...;quantity.csv') Y = read.csv('equity_price.csv') I have tried the loop Z = array() for (i in 1:2) { Z[i] = (X[[i]]*Y[i]) } # When I write this dataframe as write.csv(data.frame(Z), 'Z.csv', row.names = FALSE) When I open 'Z.csv' file, I get c.2500L..3300L..4500L..1000L..4400L.    c.14000L..45000L..48000L..26000L..15000L. 2500    14000 3300    45000 4500    48000 1000    26000 4400    15000 My requirement is to have the column heads and the portfolio total as GOOG        YHOO     Total 2500           14000     16500 3300           45000     48300 4500           4...

...ommand obj <- qcc(a[,],type="Xbar") But when I use the same command to plot the R chart and S chart jus by replacing the type="Xbar" by type="R" and Type="R" ,I am getting folloiwing error Error in limits.R(center = NA_real_, std.dev = NA_real_, sizes = c(1000L, : group size must be less than 51 when giving nsigmas In addition: Warning message: In sum(sizes * statistics) : Integer overflow - use sum(as.numeric(.)) obj<-qcc(D[trial],sizes=size[trial],type="p") Similarly I am able to plot P chart for defective dataset successfully using f...

...> obj <- qcc(a[,],type="R") > > summary(obj) > detach(final_set) > > > Here I am selecting a sample of size 1000 for each group. And it > gives me following error > > > Error in limits.R(center = 62614.0571428571, std.dev = NA_real_, > sizes = c(1000L, : > group size must be less than 51 when giving nsigmas Yes and it must be like that. You shouldn't use R charts for monitoring dispersion using sample sizes larger than 20, and for sample sizes greater than 25 you get an error. From theory, R chart used the distribution of relativ...

v1 was here: https://www.redhat.com/archives/libguestfs/2017-September/msg00135.html This is a more complete evolution of the earlier patch. It replaces most important uses of Str with PCRE throughout the code. It also extends the bindings with some useful features like case-insensitive regexps. The main places I *didn't* touch are the generator (GObject uses Str extensively); and

Skipped content of type multipart/mixed-------------- next part -------------- A non-text attachment was scrubbed... Name: PGP.sig Type: application/pgp-signature Size: 186 bytes Desc: This is a digitally signed message part Url : http://lists.samba.org/archive/rsync/attachments/20040227/923b87ee/PGP.bin

I am enclosing the patch I have been working on. I have tested this on sles9 (2.6.5) and RHEL 4 (2.6.9). The patch applies cleanly on ChangeSet 11635. This patch was cleaned up based on Ian''s and DOI''s work. Signed-off-by: K. Y. Srinivasan (ksrinivasan@novell.com) _______________________________________________ Xen-devel mailing list Xen-devel@lists.xensource.com

v1: https://www.redhat.com/archives/libguestfs/2017-September/msg00135.html v2: https://www.redhat.com/archives/libguestfs/2017-September/msg00158.html v3 is almost identical to v2, but I have added 4 extra commits to almost finish the job of replacing Str everywhere possible (note it's not possible to replace Str in common/mlstdutils or the generator because those are pure OCaml). As

>>>>> Michael Chirico >>>>> on Sun, 15 Sep 2019 20:52:34 +0800 writes: > 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

This patch series adds support for network buffering in the Remus codebase in libxl. Changes in V3: [1/5] Fix redundant checks in configure scripts (based on Ian Campbell''s suggestions) [2/5] Introduce locking in the script, during IFB setup. Add xenstore paths used by netbuf scripts to xenstore-paths.markdown [3/5] Hotplug scripts setup/teardown invocations are now

...# packages ########################## library(dplyr) library(data.table) library(IRanges) library(microbenchmark) ## prepare example dataset ########### ## use Bert's example, with 2000 stations instead of 2 d_df <- data.frame( station = rep(rep(c("one","two"),c(5,4)), 1000L), from = as.integer(c(60,61,71,72,76,60,65,82,83)), to = as.integer(c(60,70,71,76,83,64, 81, 82,83)), record = c("A","B","C","B","D","B","B","D","E&quo...

...rve <hpages at fredhutch.org> wrote: > > Hi, > > Alternatively, how about a new glance() generic that would do something > like this: > >> library(DelayedArray) >> glance <- DelayedArray:::show_compact_array > >> M <- matrix(rnorm(1e6), nrow = 1000L, ncol = 2000L) >> glance(M) > <1000 x 2000> matrix object of type "double": > [,1] [,2] [,3] ... [,1999] [,2000] > [1,] -0.8854896 1.8010288 1.3051341 . -0.4473593 0.4684985 > [2,] -0.8563415 -0.7102768 -0.9309155...

...ernatively, how about a new glance() generic that would do something >>> like this: >>> >>>> library(DelayedArray) >>>> glance <- DelayedArray:::show_compact_array >>> >>>> M <- matrix(rnorm(1e6), nrow = 1000L, ncol = 2000L) >>>> glance(M) >>> <1000 x 2000> matrix object of type "double": >>> [,1] [,2] [,3] ... [,1999] [,2000] >>> [1,] -0.8854896 1.8010288 1.3051341 . -0.4473593 0.4684985 >>> [...

...e: >> >> Hi, >> >> Alternatively, how about a new glance() generic that would do something >> like this: >> >>> library(DelayedArray) >>> glance <- DelayedArray:::show_compact_array >> >>> M <- matrix(rnorm(1e6), nrow = 1000L, ncol = 2000L) >>> glance(M) >> <1000 x 2000> matrix object of type "double": >> [,1] [,2] [,3] ... [,1999] [,2000] >> [1,] -0.8854896 1.8010288 1.3051341 . -0.4473593 0.4684985 >> [2,] -0.8563415 -0.71...

...ibrary(dplyr) > library(data.table) > library(IRanges) > library(microbenchmark) > > ## prepare example dataset ########### > > ## use Bert's example, with 2000 stations instead of 2 > d_df <- data.frame( station = rep(rep(c("one","two"),c(5,4)), 1000L), > ??????????????????? from = as.integer(c(60,61,71,72,76,60,65,82,83)), > ??????????????????? to = as.integer(c(60,70,71,76,83,64, 81, 82,83)), > ??????????????????? record = c("A","B","C","B","D","B","B","D&qu...

Hola Javier, Intenta los siguientes cambios g4 <- function(codigo_llega, n_caracteres){ codigo_llega <- as.character(codigo_llega) if(n_caracteres == 6) res <- substr(codigo_llega, start=0, stop=4) else res <- codigo_llega res } g4 <- Vectorize(g4) x <- c('Jorge Velez','Javier Marcuzzi','Daniel Merino','Susana deus Alvarez', 'Carlos