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Hi, LLVM 2.1-pre1 test results: Linux (SUSE) on x86 (P4) Release mode, but with assertions enabled LLVM srcdir == objdir # of expected passes 2250 # of expected failures 5 I ran the llvm-test suite on my desktop while I was also working on that PC, so don't put too much trust in the timing info. Especially during the "spiff" test the machine was swapping

Please help: I am using qplot as below and want to specify a different color scheme for race but dont know how, can someone show me. Thanks in advance Code and input file below: library(ggplot2) library(gridExtra) d<-read.table("results", header=TRUE, fill=TRUE) plot2<-qplot(X,Y,data=d,color=race,facets=TYPE~., xlab="X", ylab="Y") + theme_bw()

LLVMers, The 2.1 pre-release (version 1) is available for testing: http://llvm.org/prereleases/2.1/version1/ I'm looking for members of the LLVM community to test the 2.1 release. There are 2 ways you can help: 1) Download llvm-2.1, llvm-test-2.1, and the appropriate llvm-gcc4.0 binary. Run "make check" and the full llvm-test suite (make TEST=nightly report). 2) Download

f <- (structure(list(X = structure(96:97, .Label = c("119DAmm", "119DN", "119DNN", "119DO", "119DOC", "119Flow", "119Nit", "119ON", "119OPhos", "119OrgP", "119Phos", "119TKN", "119TOC", "148DAmm", "148DN", "148DNN", "148DO",

Hello at all, working with a dataset I try to optimize a non-linear function with constraint. test<-read.csv2("C:/Users/Herb/Desktop/Opti/NORM.csv") fkt<- function(x){ a<-c(0) s<-c(0) #Minimizing square error for(j in 1:107){ s<-(test[j,2] - (x[1] * test[j,3]) - (x[2] * test[j,4]) - (x[3]*test[j,5]) - (x[4]*test[j,6]) - (x[5]*test[j,7]))^2 a<- a+s} a<-as.double(a)

LLVMers, The 2.5 pre-release is available for testing: http://llvm.org/prereleases/2.5/ If you have time, I'd appreciate anyone who can help test the release. Please do the following: 1) Download/compile llvm source, and either compile llvm-gcc source or use llvm-gcc binary (please compile llvm-gcc with fortran if you can). 2) Run make check, send me the testrun.log 3) Run "make

Hi all, I have investigated the compile-time overhead of "Polly Scop Detection" pass based on LNT testing results. This mail is to share some results I have found. (1) Analysis of "SCOP Detection Pass" for PolyBench (Attached file PolyBench_SCoPs.log) Experimental results show that the "SCOP Detection pass" does not lead to significant extra compile-time

Hello, Can the x and y axis limits be specified in a density plot with the mixtools package for a finite mixture model? Uncommenting the xlim2/ ylim2 lines in the plot command below generates 'not a graphical parameter' warnings (and does not change the axis settings), and uncommenting the xlim/ylim lines generates a 'formal argument "ylim" matched by multiple actual

I'm a little further along now. I've started to put together a simple driver for Elsa and LLVM that I'm calling "ellsif" (cute name, I think it works). The file being compiled is a "printf" function. Here are timing results for optimized and unoptimized runs: [~/elsa/ellsif] dev% ./ellsif -v test/ofmt.i -time-actions Adding test/ofmt.i as a preprocessed C file

Hi there, I have a question on how to extract the linear term in the penalized spline in coxph. Here is a sample code: n=100 set.seed(1) x=runif(100) f1 = cos(2*pi*x) hazard = exp(f1) T = 0 for (i in 1:100) { T[i] = rexp(1,hazard[i]) } C = runif(n)*4 cen = T<=C y = T*(cen) + C*(1-cen) data.tr=cbind(y,cen,x) fit=coxph(Surv(data.tr[,1],

On 06/29/2013 05:04 PM, Star Tan wrote: > Hi all, > > > > I have investigated the compile-time overhead of "Polly Scop Detection" pass based on LNT testing results. > This mail is to share some results I have found. > > > (1) Analysis of "SCOP Detection Pass" for PolyBench (Attached file PolyBench_SCoPs.log) > Experimental results show that the

This may not actually be an R/Splus problem, but it started off that way ..... ===+===+===+===+===+===+===+===+===+===+===+===+===+===+===+===+===+===+=== Executive summary: ================== Simulations involving extreme value distributions seem to ``work'' when the underlying distribution is exponential(1) or exponential(2) == chi-squared_2, but NOT when the underlying distribution is

At 2013-06-30 08:34:34,"Tobias Grosser" <tobias at grosser.es> wrote: >On 06/29/2013 05:04 PM, Star Tan wrote: >> Hi all, >> >> >> >> I have investigated the compile-time overhead of "Polly Scop Detection" pass based on LNT testing results. >> This mail is to share some results I have found. >> >> >> (1) Analysis

Estimados Escribo para consultar sobre el uso de modelos mixtos anidados. Los datos que estoy analizando provienen de censos de malezas en cuatro tipos de paisajes de la región pampeana, en los que seleccioné al azar igual número de lotes agrícolas cultivados con tres cultivos (maíz, soja y trigo-soja). En cada lote censé el número de especies de malezas en tres posiciones: el alambrado, el borde

Target: FreeBSD 6.2-STABLE on i386 autoconf says: configure:2122: checking build system type configure:2140: result: i386-unknown-freebsd6.2 [...] configure:2721: gcc -v >&5 Using built-in specs. Configured with: FreeBSD/i386 system compiler Thread model: posix gcc version 3.4.6 [FreeBSD] 20060305 [...] objdir != srcdir, for both llvm and gcc. Release build. llvm-gcc 4.2 from source.

LLVMers, The 2.2 prerelease is now available for testing: http://llvm.org/prereleases/2.2/ If anyone can help test this release, I ask that you do the following: 1) Build llvm and llvm-gcc (or use a binary). You may build release (default) or debug. You may pick llvm-gcc-4.0, llvm-gcc-4.2, or both. 2) Run 'make check'. 3) In llvm-test, run 'make TEST=nightly report'. 4) When