similar to: [LLVMdev] Fw: Fix for forward-declare-class.cpp in debuginfo-tests

Hi, I'm not sure where to post this since it relates to the "debuginfo-tests" repository, so please tell me where to forward it if this is the wrong place! I've fixed the forward-declare-class.cpp test-case in the attached patch. It seems that gdb has fixed their bug in version 7.4, so the patch uses a regex to match expected output for both the broken and fixed gdb. Cheers

On Tuesday, July 31, 2012 7:19 PM, Andy Gibbs wrote: > I'm afraid it looks like commit r160899 has broken two test-cases > which are part of the "debuginfo-tests" repository. I believe I > have narrowed it down to the correct commit... Erm, but I didn't post the details (obviously too much in a rush to get home last night! My apologies!) So here it is: The tests that

Hi, I'm afraid it looks like commit r160899 has broken two test-cases which are part of the "debuginfo-tests" repository. I believe I have narrowed it down to the correct commit... Cheers Andy

Hi again, Any interest in this patch to incorporate lld testing into "make check-all" when lld built as part of llvm? Is this the best place to post patches for lld? I've attached it again here to include a missing dependency. Cheers Andy On Thursday, July 26, 2012 1:45 PM, Andy Gibbs wrote: > Hi, > > Attached is a patch which incorporates the lld test-suite into

> *NOTE* IIRC compiling this with -O0 on x86-64 can yield the wrong result > since clang will emit shifts and on intel shifts are mod the register > size [...snip...] I remember finding the same thing (although I haven't tried it on a recent clang version) and what I wondered was whether there was mileage in having an explicit intrinsic for rotation (like there is for bit counting,

I have been using the rtmvt function in the {tmvtnorm} package i'm getting the warning: "Acceptance rate is very low and rejection sampling becomes inefficient. Consider using Gibbs sampling." but i AM specifying the gibbs algorithm!!: rtmvt(M, mean=q[,,i,j], sigma=((u[i,j] + nu[i])/(p+nu[i]))*delta[,,i], df=ceiling(nu[i]+p), lower=c(0,0), algorithm="gibbs") Any

I have downloaded the Umacs (Universal Markov chain sampler) and submitted the following sample code from Kerman and Gelman.   s <-Sampler( J=8, sigma.y  =c(15,10,16,11,9,11,10,18),           y  =c(28, 8,-3,7,-1,1,18,12),      theta =Gibbs(theta.update,theta.init),           V =Gibbs(V.update,mu.init),         mu =Gibbs(mu.update,mu.init),         tau =Gibbs(tau.update,tau.init),       

Hi, I want to use R_qsort_int_I() in my C function, but getting the following error. It looks like there is a conflict between Rmath.h, which I use to generate random numbers, and R_ext/Boolean.h I would appreciate any help to fix this problem. gcc -ansi -g -o Gibbs gibbs.c subroutines.o rand.o vector.o -lm -lRmath -llapack -lblas -lfrtbegin -lg2c -lm -shared-libgcc In file included from

Hello, thank you very much for your previous answers about the C++ code. I am interested in the application of the Gibbs Sampler in the IRT models, so in the function MCMCirt1d and MCMCirtkd. I've found the C++ source codes, as you suggested, but I cannot find anything about the Gibbs Sampler. All the files are for the Metropolis algorithm. Maybe I am not able to read them very well, by the

Hello! I was wondering if it's possible to see the actual code of checkTmvArgs function that is part of the code for rtmvnorm (which is below - I just typed "rtmvnorm" on the prompt). I get an error: Error in checkTmvArgs(mean, sigma, lower, upper) : sigma must be a symmetric matrix At the same time I am pretty sure that the matrix I am passing as sigma is a var-covar matrix

Hi, Please could someone kindly review this patch for me? Many thanks Andy On Wednesday, July 18, 2012 4:29 PM, Andy Gibbs wrote: > Hi, > > Attached is a simple patch to fix a "Bad fd number" error in > test_debuginfo.pl which has come about because of an invalid redirection > in the system call. > > Normally the stdout+stderr to file redirection is

Dear all, I'm fairly new to Samba and CIFS and, for that matter, Linux in general. I'm having a problem with "mount.cifs" as provided with Debian 5. I'm afraid I cannot say what version of mount.cifs I have since doing "mount.cifs -V" does not (contrary to the message it shows when I do this) actually show the version, but rather how to use the program. The

I try to fit a LDA model to a TermDocumentMatrix with the topicmodels package... but R says: > Error in LDA(TDM, k = k, method = "Gibbs", control = list(seed = SEED, : > x is of class ?TermDocumentMatrix??simple_triplet_matrix? > class(TDM) > [1] "TermDocumentMatrix" "simple_triplet_matrix" I try to use a matrix... but don't work: > MAT

Hello R community, following is my code and it shows error, can some one fix this error and explain why this occurs? gibbs <-function(m,n, theta = 0, lambda = 1){ alpha <- 1.5 beta <- 1.5 gamma <- 1.5 x<- array(0,c(m+1, 3)) x[1,1] <- theta x[1,2] <- lambda x[1,3]<- n for(t in 2:m+1){ x[t,1] <- rbinom(x[t-1,3], 1, x[t-1,1])

Hi, I have to draw samples from an asymmetric-Laplace-Normal distribution: f(u|y, x, beta, phi, sigma, tau) \propto exp( - sum( ( abs(lo) + (2*tau-1)*lo )/(2*sigma) ) - 0.5/phi*u^2), where lo = (y - x*beta) and y=(y_1, ..., y_n), x=(x_1, ..., x_n) -- sorry for this huge formula -- A WinBUGS Gibbs sampler and the HI package arms sampler were used with the same initial data for all parameters. I

Dear R-users I have a problem choosing initial points for the function arms() in the package HI I intend to implement a Gibbs sampler and one of my conditional distributions is nonstandard and not logconcave. Therefore I'd like to use arms. But there seem to be a strong influence of the initial point y.start. To show the effect I constructed a demonstration example. It is reproducible

I have the following code, gibbs <-function(m,theta = 0.25, lambda =0.55, n =1){ alpha <- 1.5 beta <- 1.5 gamma <- 1.5 x<- array(0,c(m+1, 3)) x[1,1] <- theta x[1,2] <- lambda x[1,3]<- n for(t in 2:(m+1)){ x[t,1] <- rbinom(1, x[t-1,3], x[t-1,1]) x[t,2]<-rbeta(1, x[t-1,1] + alpha, x[t-1,3] - x[t-1,1] + beta) x[t,3]

Dear all, I'm currently experiencing big troubles with my C code called by .Call in a R function. I know this may not be the right place for such things but these errors are driving me crazy. And I hope other people may have already experienced these problems so that they could give me good suggestions. Sorry if I'm completely out of topic. Well let's go... I'm writing a

I am writing a Gibbs sampler. I think it is outputting some of what I want, in that I am getting vector of several thousand values (but not 10,000) in a txt file at the end. My question is, is the error message (see below) telling me that it can't output 10,000 values (draws) because of a limitation in my memory, file size, shape etc, or that there is an error in the sampler itself? >

Hello ALL! Some times ago I started to learn and play with Bayesian stuffs. Many advice use of WinBUGS for Bayesian inference Using Gibbs Sampler. However, WinBUGS is discontinued, and now, development is under OpenBUGS. I wasn't lazy, so I installed both and tried out. In more than 90% of cases they give comparable outcome. But in few cases I got substantial differences. Recently, I read nice