similar to: bigkmeans not parallel

Hi, all, I have a really big matrix that I want to run k-means on. I tried: >data <- read.big.memory('mydata.csv',type='double',backingfile='mydata.bin',descriptorfile='mydata.desc') I'm using doMC to register multicore. >library(doMC) >registerDoMC(cores=8) >ans<-bigkmeans(data,k) In system monitor, it seems only one thread running R. Is

Hi, I have a 60k*600k matrix, which exceed the vector length limit of 2^32-1. But it's rather sparse, only 0.02% has value. So I save is as MarketMatrix (mm) file, it's about 300M in size. I use readMM in Matrix package to read it in. If do so, the data type becomes dgTMatrix in 'Matrix' package instead of the common matrix type. The problem is, if I run k-means only on part of

I know this is not a revolution support forum, but as anyone noticed the following? I have a foreach loop to generate random samples. If I run the exact code below in normal r (2.14.1) it works as expected, but if I run it from revolution 4.2.0 each loop returns the same numbers. The only way I can get revolution to give different numbers is using 1 instead of 8 in registerDoSNOW(makeCluster(8,

Buen día, no sé si estoy utilizando bien la lista, es la primera vez. Si lo hago mal me corrigen por favor. Sobre tu comentario Pedro, muchas gracias. Lo qeu entiendo con tu sugerencia de set.seed es qeu de esa forma fijas los resultados, pero no estoy seguro si otra agrupación funcione mejor. Es decir me interesa un método de agrupación que genere la "mejor" agrupación y como los

Hola, pues con esto del kmeans ando pegándome ahora y si quieres tener los mismos resultados para los mismos datos de entrada debes darle una semilla constante en cada ejecución: set.seed(1234) Como se explica aquí: https://stat.ethz.ch/pipermail/r-help/2007-March/128671.html Lo he comprobado en muchas ejecuciones y es así. Otra posibilidad que se menciona también en las consultas que he

Hi, I want to impute a data set multiple times with Amelia, but the data set is large so it takes a long time. As a result, I'm trying to run the multiple imputation with parallel processors in Windows, but am having trouble. Here is a quick example: ###### library(foreach) library(doSNOW) registerDoSNOW(makeCluster(4, type = "SOCK")) getDoParWorkers() getDoParName()

After a year my R programming style is still very "C like". I am still writing a lot of "for loops" and finding it difficult to recognize where, in place of loops, I could just do the same with one line of code, using "sapply", "lapply", or the like. On-line examples for such high level function do not help me. Even if, sooner or later, I am getting my R

Hello colleagues, I have tried to use the package bigmemory, biganalytics and biglm. I want to specify a multivariate regression with a weight. I have imported a large dataset with the library(bigmemory). I load the library (biglm) and specified a regression with a weight. But I get everytime an error message like "object not found" or "`weights' must be a

Hello, here is a working reproducible example which crashes R using kmeans or gives empty clusters using the nstart option with R 15.2. library(cluster) kmeans(ruspini,4) kmeans(ruspini,4,nstart=2) kmeans(ruspini,4,nstart=4) kmeans(ruspini,4,nstart=10) ?kmeans either we got empty always clusters and or, after some further commands an segfault. regards, Detlef Groth ------------ [R] Empty

Hi. I am trying to modify kmeans function. It seems that is failing something obvious with the workspace. I am a newbie and here is my code: myk = function (x, centers, iter.max = 10, nstart = 1, algorithm = c("Hartigan-Wong", + "Lloyd", "Forgy", "MacQueen")) + { + do_one <- function(nmeth) { + Z <- switch(nmeth, { + Z

aggregate.ts does not behave in the same way as the equivalent method aggregate.rts in S-PLUS. In particular it - changes the start of the time series - tends to have a length which is 1 shorter For example: R> x <- ts(1:10) R> aggregate(x, nfreq=0.5, FUN=min) Time Series: Start = 2 End = 8 Frequency = 0.5 [1] 2 4 6 8 S> x <- rts(1:10) S> aggregate(x, nf=0.5, fun = min) [1]

From: Maros Zatko <mzatko@redhat.com> --- fish/rl.c | 158 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ fish/rl.h | 32 +++++++++++++ 2 files changed, 190 insertions(+) create mode 100644 fish/rl.c create mode 100644 fish/rl.h diff --git a/fish/rl.c b/fish/rl.c new file mode 100644 index 0000000..bb8fd62 --- /dev/null +++ b/fish/rl.c @@ -0,0 +1,158 @@ +/* guestfish -

Hello, I am an R newbie, trying to use lsoda to solve standard Lotka-Volterra competition equations. My question is: how do I pass a parameter that varies with time, like say, phix <- 0.7 + runif(tmax) in the example below. # defining function lotvol <- function(t,n,p){ x <- n[1]; y <- n[2] rx <- p["rx"]; ry <- p["ry"] Kx <-

Hi. It seems that nearly every time I try to use factanal I get the following response: >faa2db1<-factanal(mretdb1,factors=2,method="mle",control=list(nstart=25)) Error in factanal(mretdb1, factors = 2, method = "mle", control = list(nstart = 25)) : unable to optimize from these starting value(s) > In the case cited above, mretdb1 is synthetic data created

I''ve created a simple script (below) which calls the effect.appear script in order to make a group of items appear at the page load. I would like to have the images randomly appear at different times; e.g. the 3rd image might start appearing 2 seconds after load, the 6th image immediately after load, the first image 1 second after load, etc... How can I achieve this affect? ---

Dear List, I'm trying to get diff/removed data and it's offset out. So I write a functions in receive_data. When I run backup, I found there is a weird behavior which I don't understand. i = recv_token(f_in, &data) will receive (i = -1, offset2 = 0) some where in the middle of the transfer procedure. That's to say, it's going to transfer the first data block from sender,

Hello, the k-means {stats} help and the Hartigan&Won paper say nothing about the way random sets works (parameter nstart). I would expect to get the different results for each random initial set but I always obtain only one result: how is it selected? Charles Raux

Dear experts, I am encountering a version-dependent issue. My laptop runs Ubuntu 12.04 LTS 64-bit, R 2.14.1; the issue explained below never occurred with this version of R My desktop runs Ubuntu 11.10 64-bit, R 2.13.2; what follows applies to this setup. The data I'm clustering is constituted by the rows of a 320 x 6 matrix containing integers ranging from 1 to 7, no missing data. I applied

Hi, I am running some rather complex mixtures of beta regressions using the betamix() command from the betareg package (V. 3.0-4). If I am doing exploratory regressions with only one random starting value (nstart=1) I obtain results which converge after about 100 iterations. However, if I run regressions with nstart=100 random starting values I obtain solutions amended with a warning that no

> tab <- array( sample(3^5), rep(3,5) ) > loglin( tab, list(1,2,3,4,5) )[[1]] # AOK 2 iterations: deviation 5.456968e-12 [1] 10909.89 > loglin( tab, list(1,2,3,4,5), c(1,2,3) )[[1]] # OUCH! Process R bus error at Wed Jul 21 17:03:55 2004 this is inconsistent - sometimes issuing this msg several times before barfing: Error in switch(z$ifault, stop("This should not