similar to: slice sampler

Good to hear you've made such progress. Just a couple of comments: - You should use points3d() rather than rgl.points(). The latter is a low level function that may have unpleasant side effects, especially mixing it with other *3d() functions like persp3d(). - There are several ways to draw a flat surface to illustrate your data. Which one to use really depends on the form of data.

Dear All, Thanks a lot for the useful help again. I manage to get it done up to a point where I think I just need to apply some smoothing/interpolation to get denser points, to make it nice. Basically, I started from Duncen's script to visualize and make the clipping along a plane at a slice. Then I map my data points' values to a color palette and just plot them as points on this plane.

libswfdec/swfdec_codec_gst.c | 2 libswfdec/swfdec_font.c | 4 libswfdec/swfdec_js_movie.c | 39 ++++++++- libswfdec/swfdec_morph_movie.c | 18 ++-- libswfdec/swfdec_pattern.c | 130 +++++++++++++++++++------------ libswfdec/swfdec_pattern.h | 16 ++- libswfdec/swfdec_shape.c | 99 +++++++++++++----------- libswfdec/swfdec_shape.h | 11 +-

I have a confusing problem with getting the form `x - trunc(x)' to be exactly zero when `x' is an integer. It only seems to occur inside of a function. [R-1.6.0 on Linux/Intel] I have a function to return the highest precision digit of values in `x': prec<-function(x){ init <- trunc(log10(max(x))) y <- x - trunc(x) while (any(y > 0)) { init <- init - 1 x1

Hi Ilia. It looks like there is a hardware bug on Fermi and Kepler where TLD unconditionally uses the sampler from slot 0. This is supposedly fixed in Maxwell. What I could find internally suggests the bug wasn't present on Tesla, but let me know if your observations contradict that. The only work around is to have a sampler defined and bound. Further, be careful to have reasonable state

Hi, I'm having issues with what I believe is a bug in the MCMCpack's MCMCmetrop1R function. I have code that basically looks like this: posterior.sampler <- function(data, prior.mu){ log.posterior <- function(theta) log.likelihood(data, theta) + log.prior(prior.mu, theta) post.samples <- MCMCmetrop1R(log.posterior, theta.init=prior.mu, burnin=100, mcmc=1000, thin=40,

Hi there, I just installed the package under windows, and it works very well. However, as I tried to install it on a cluster which uses linux OS, "Linux lionxc.aset.psu.edu 2.6.18-194.el5 #1 SMP Tue Mar 16 21:52:39 EDT 2010 x86_64 x86_64 x86_64 GNU/Linux" I always failed. The message during the installation process is as follows: > install.packages("geepack") Warning

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),       

Hello, This question is in the context of Tesla / Fermi generations, which have explicit bindings for textures / samplers. It might also apply to Kepler+, not quite as sure due to the bindless nature. I've been trying to understand how the TLD operation works (which is used to implement texelFetch in GLSL). It does not appear to the op takes an explicit sampler id at all (unlike all the

Hi Andy, Thanks for checking! I do see an issue on Tesla as well (at least G92, and I believe someone else reported on a GT215 or GT218). However I haven't confirmed that it's the identical issue to what I see on Fermi with quite as much certainty as what I've checked on a GF108. (For the G92, the texture buffer object test fails in the same way it does on Fermi, but there could be

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? >

Hi there, I had a serious problem here . Consider the following Bayesian model(discretized variance gamma): #Likelihood J[i]<-lambda*G[i]+sigma*sqrt(G[i])*rnorm(0,1) G[i]<-rgamma(1/nu,1/nu) #Prior: nu<-rinvgamma(m,M) # Parameters lambda=-.04 ; sigam=.38; nu=6.48; m=10,M=10 ; T=5000 (length of data) An author claimed that he got posterior distribution

Hello everyone, I have some binary, spatially autocorrelated data I would like to run autologistic regression on. I hope to incorporate both ordinary covariates (environmental predictors) and a spatial autocovariate in the model, ideally with a second-order neighbourhood structure. Since my computing skills are limited, I am wondering if anyone has composed an algorithm for this purpose, and

Hey everyone, I have read the paper by Ritter and Tanner(1992) on Griddy-Gibbs sampler and I am trying to implement it in R without much luck. I was wondering if anyone had used this or could point me to any example code. Thanks, Liz --------------------------------- [[alternative HTML version deleted]]

Hola! It is somewhat inconvenient to use plot.function, when add=TRUE. The following (miniscule) change makes it behave better: plot.function <- function (fn, from , to, xlim = NULL, ...) { if (!is.null(xlim)) { if (missing(from)) from <- xlim[1] if (missing(to)) to <- xlim[2] } curve(fn, from, to, xlim = xlim, ...) } The only

Hi R collective, I quite like the "curve" function because you can chuck a R function into it, and see the graph in one line of R. I had a google and found some threads for filling under the line; http://tolstoy.newcastle.edu.au/R/e2/help/07/09/25457.html However they seem to miss the point of the simplicity of going, "I wonder what that looks like, and can I have some colour

Hi All, I have got one domination error after running LoopVectorizer with VPlanNatviePath. Let's see simple IR snippet after loop vectorization with VPlanNatviePath. vector.body: ... br label %for.body10.preheader67 for.body10.preheader67: ; preds = %for.cond.cleanup972, %vector.body %vec.phi = phi <4 x i64> [ zeroinitializer, %for.cond.cleanup972 ], [ %8,

Hello everyone, please I need your help. To reproduce my problem I created simple pass for backends (TestPass.cpp in attached files). That pass I call from Mips backend in this way (MipsTargetMachine.cpp): bool MipsPassConfig::addPreRegAlloc() { addPass(createTestPass()); return false; } The problem becomes, when I am trying compile file ldtoa.ll (in attached files). Compiling

Hello everyone! First of all, please note that I'm working under Windows 7. I have written a Gibbs sampler in R and I'm now in the process of translating it in C++ to increase the speed. I'm relatively new to C++ and this question may be trivial, but my search so far have been unsuccessful. In my Gibbs sampler I am using some basic R functions (like the rep function for example)

Hi, We've been working for the last two years on support for ARM's Scalable Vector Extension in LLVM, and we'd like to upstream our work. We've had to make several design decisions without community input, and would like to discuss the major changes we've made. To help with the discussions, I've attached a technical document (also in plain text below) to describe the