similar to: An idea: Extend mclapply's mc.set.seed with an initial seed value?

Hi everybody, I am new in e1071 and with SVMs. I am trying to understand the performance of SVMs but I face with a situation that I thought as not meaningful. I added the R code for you to see what I have done. /set.seed(1234) data <- data.frame( rbind(matrix(rnorm(1500, mean = 10, sd = 5),ncol = 10), matrix(rnorm(1500, mean = 5, sd = 5),ncol = 10))) class <- as.factor(rep(1:2,

Now I am getting confused. I see two postings from me in the archives: https://stat.ethz.ch/pipermail/r-devel/2015-May/071205.html https://stat.ethz.ch/pipermail/r-devel/2015-April/070982.html Were these actually published to the list? If so - big apology. Regards, Ivan On Fri, May 29, 2015 at 12:43 AM David Winsemius <dwinsemius at comcast.net> wrote: > > On May 28, 2015, at 9:11

I'm using an SVM as I've seen a paper that reported extremely good results. I'm not having such luck. I'm also interested in ideas for other approaches to the problem that can also be applied to general problems (no assuming that we're looking for spirals). Here is my code: library(mlbench) library(e1071) raw <- mlbench.spirals(194, 2) spiral <-

Hello, This turned out to be surprisingly hard for me: Let's say I have mm = matrix(as.character(seq(1,30, 1)), nrow=3); mm [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [1,] "1" "4" "7" "10" "13" "16" "19" "22" "25" "28" [2,] "2" "5"

Hello, I have tried a few searches without luck before posting, since this one seems a pretty basic question. I am using R 2.7.0 on WinXP, as I have long started using this version for my thesis work and am reluctant to update fearing consistency/backward compatibility issues could happen. I noticed that whenever I start an R session (launch the console application) and run a script involving

Let me start with the code: library(quantmod) library(rugarch) getSymbols("SPY", from="1900-01-01") rets=na.trim(diff(log(Cl(SPY)))) tt = tail(rets["/2004-10-29"], 1000) spec = ugarchspec(variance.model=list(garchOrder=c(1,1)), mean.model=list(armaOrder=c(2,5)), distribution.model="sged") for(ii in 1:10) { ttFit = ugarchfit( spec=spec,

Support vector machines (SVMs) have established themselves as one of the preeminent machine learning models for classification and regression over the past decade or so, frequently outperforming artificial neural networks in task such as text mining and bioinformatics. Dr. Lutz Hamel, author of "Knowledge Discovery with Support Vector Machines" from Wiley will present his online course

Hello, Over the last two months I have sent two messages (same topic) to the list. None of them showed on the list. For the first, I got a message that it is in some queue and waiting for an administrator to look at it. Is the queue THAT long?! [[alternative HTML version deleted]]

Dear All, I've a question about scaling the input variables for an analysis with svm (package e1071). Most of my variables are factors with 4 to 6 levels but there are also some numeric variables. I'm not familiar with the math behind svms, so my assumtions maybe completely wrong ... or obvious. Will the svm automatically expand the factors into a binary matrix? If I add numeric

Hello, I have some data, and I want to generate random numbers following the distribution of this data (in other words, to generate a synthetic data set sharing the same stats as a given data set). Reading an old thread I found the following text: >If you can compute the quantile function of the distribution (i.e., the >inverse of the integral of the pdf), then you can use the

For reasons that are best known to myself [ ;-) ] I have a value of .Random.seed saved (via dput()) in a file ``.Random.seed.save''. In my .Rprofile I have the lines: .Random.seed <- dget(".Random.seed.save") Junk <- dget(".Random.seed.save") print(all.equal(.Random.seed,dget(".Random.seed.save")))

Hallo, I have a question concerning SVM regression in R. I intend to use SVMs for feature selection (and knowledge discovery). For this purpose I will need to extract the weights that are associated with my features. I understand from a previous thread on SVM classification, that predictive models can be derived from SVs, coefficiants and rhos, but it is unclear for me how to transfer this

Hello, I am doing intensive tests on SVMs parameter selection. Once a while I got the error: Error in save.image(): image could not be renamed and is left in .RDataTmp1 I cannot use the information saves in .RDataTmp1. When that happens I loose several hours of tests. It happens, ussualy when the computer is locked, i.e., there is not other relevant processes running on. I can do tests and get

I want to start R processes on multiple processors from single shell script and I want all of them to have different random seeds. One way of doing this is sleep 2 # (with 'sleep 1' I am often getting the same number) ... set.seed(unclass(Sys.time())) Is there a simpler way without a need to sleep between invoking different R processes ? Ryszard

I would like to suggest a modification of initial scrambling of the seed in RNG_Init (called from do_setseed). The modified code is equivalent, but faster. Patch against R-devel_2007-09-22 follows --- R-devel-orig/src/main/RNG.c 2007-09-02 07:49:35.000000000 +0200 +++ R-devel-modif/src/main/RNG.c 2007-09-23 10:51:59.234566440 +0200 @@ -216,8 +216,8 @@ BM_norm_keep = 0.0; /* zap Box-Muller

library(xts) library(TTR) ndx = getYahooData("^NDX") aa = ndx$Close bb = aggregate(aa, as.yearweek, tail, 1) The last operation takes forever, and then the bb dates are messed up. The following produces the desired result: time(aa) = as.Date(time(aa)) bb = aggregate(aa, as.yearweek, tail, 1) The index of ndx and aa is of POSIXct (as reported by is(time(ndx))) , which apparently

Dear expeRts, I struggle with the following problem using snow clusters for parallel computing: I would like to specify l'Ecuyer's random number generator. Base R creates a .Random.seed of length 7, the first value indicating the kind fo random number generator. I would thus like to use the components 2 to 7 as the seed for l'Ecuyer's random number generator. By doing so, I

Hi, this is related to a question just raised on Bioconductor where one function sets the random seed internally but never resets it, which results in enforced down streams random samples being deterministic. What is the best way to reset the random seed when you use set.seed() within a function? Is it still to re-assign '.Random.seed' in the global environment as following example

hello all, I use the R standalone math library in my own C program, and the default R random number generator can be seeded with set_seed(const unsigned int, const unsigned int). How could I seed the RNG with an instance of .Random.seed ? I would need this or a similar workaround for debugging purposes. More precisely, I use the default R random number generator to sample from various

Hi. Suppose I have a function which does some random number generation within. The random number generation inside the function changes the value of .Random.seed in the calling environment. If I want to restore the pre-function call .Random.seed, I can do: save.seed<-.Random.seed result<-myfunction() .Random.seed<-save.seed Is there a way to do the restoration inside the function?