search for: artifical

Usually the y-axis is shown on the left-hand-side of a graph, is it possible to artifically creat one more y-axis on the right-hand-side in R? What is the main reference? Thank you in advance. [[alternative HTML version deleted]]

...actually “work” or be “worth it”, > we won’t know unless we try, and that won’t fulfill its promise if > there are compromises to Camp B. It has been said in this thread before, but I fail to see how the atom model is an actual improvement over the fine grained section model. It seems to be artifically restricted for no good reasons. > Lets stop thinking about lld as one linker, and instead think of it is > two different ones. We’ll build a Camp B linker which is the best of > breed section based linker. It will support linker scripts and do > everything better than any existing...

> On Apr 24, 2017, at 11:00 AM, Daniel Berlin <dberlin at dberlin.org> wrote: > > > > On Mon, Apr 24, 2017 at 10:23 AM, David Jones via llvm-dev <llvm-dev at lists.llvm.org <mailto:llvm-dev at lists.llvm.org>> wrote: > How do you disable optimization for a function? > > I ask because my application often compiles machine-generated code that results in

On May 4, 2015, at 1:16 PM, Joerg Sonnenberger <joerg at britannica.bec.de> wrote: > It has been said in this thread before, but I fail to see how the atom > model is an actual improvement over the fine grained section model. It > seems to be artifically restricted for no good reasons. Sections come with a huge amount of bloat and overhead that atoms do not. >> Lets stop thinking about lld as one linker, and instead think of it is >> two different ones. We’ll build a Camp B linker which is the best of >> breed section based l...

Hello, am I using 'setIs' in the correct way in the subsequent (artifical) example? Do I have to specify explicit 'setAs' for 'list' and 'vector' or should this work automatically, since "getClass("List1")" states an explicit coerce also for these classes. I'm working with R 2.0.0 Patched (2004-10-06) on windows 2000. Th...

...nformation down. This now leaves out the question whether the command line view and the resolver view should have the same classes or not. I am not at the point yet where I can tell what the best behavior is. I can see good reasons for a strict separation in the class tree, but it may also be more artifical as separation than necessary. Joerg

...t generates a few variations of stuff that was interesting >> to scheduling algos. It would just take someone to setup a proper >> test-suite and a bot for it and I'd happily contribute more tests :) > > Well, I find limited motivation to "optimise" the compiler for artifical > code. Trying to identify real world code that triggers expensive > behavior is a much more useful exercise, IMO. Well my practical experience so far has been that the function size for most compiler inputs stays at a reasonable/smallish compared to how much memory most computers have. I as...

...quot;stops there". E.g., if you *nest* two sapply() calls where the inner one produces a matrix, very often the logical behavior would be for the outer sapply() to stack these matrices into an array of rank 3 ["array rank"(x) := length(dim(x))]. However it does not do that, e.g., an artifical example p0 <- function(...) paste(..., sep="") myF <- function(x,y) { stopifnot(length(x) <= 3) x <- rep(x, length.out=3) ny <- length(y) r <- outer(x,y) dimnames(r) <- list(p0("r",1:3), p0("C", seq_len(ny))) r } and >...

...stion? [probably not, but ..] The homework question & answer would be Q: What is the best fitting 3D copula if you have perfectly correlated ("rank 1") data? A: The complete-dependence copula ... which is indeed a boundary case, hard to imagine you'd encounter for non-artifical data. Another hint: > fitCopula(normalCopula(,3), pobs(data)) Call: fitCopula(copula, data = data) Fit based on "maximum pseudo-likelihood" and 200 3-dimensional observations. Copula: normalCopula rho.1 1 The maximized loglikelihood is 3686 Optimization converged > -------...

...nes because it measures the general overhead of setting up all the “infrastructure” (static initializers, creating a pass pipeline, etc.) That would indeed be a very interesting test, however this will be way too short to measure predictably on its own. I could see it working if we had a flag that artifically runs the compilation pipeline hundreds of times or alternatively puts the whole compiler into something like googlebenchmark. - Matthias > > — > Mehdi > > > > >> On Nov 15, 2016, at 1:27 PM, Gerolf Hoflehner via llvm-dev <llvm-dev at lists.llvm.org <mailto...

Hello! I am trying to fit a copula to some data in R and I get the error mentioned above. This is the code for a reproducible example - library(copula) data = matrix(data=runif(600),nrow=200,ncol=3) data[,2] = 2*data[,1] data[,3] = 3*data[,1] fr_cop = frankCopula(dim=3) fit_fr_cop = fitCopula(fr_cop,pobs(data),method = "mpl") #Error Here The error says : Error in fitCopula.ml(copula, u

...ch dimension [I also chose cex=5 to make it large enough so that it should (theoretically) be visible]. My wireframe plot is a complicated function which I cannot post here. Below is a minimal example, however, it didn't show the same problem [the surface is too nice I guess]. I therefore *artifically* create the problem in the example below so that you know what I mean. For one of the points, I subtract an epsilon [=0.25] in z-direction and suddenly the point completely disappears. The strange thing is that the point is not even "under" the surface [use the screen-argument to rot...

Let me take an artifical matrix : dat = matrix(rnorm(200*200), 200, 200) My goal is to visualize this matrix according to the procedure, described in previous mails. I took Mendelssohn's advice and got following advice : ?plot.im Z <- setcov(owin()) plot(Z) .................... etc However I...

...Predicted Actual nonspam spam nonspam 778 43 spam 63 496 # Calucate overall error percentage ~ 7.68% >(nn1.test.perf <- 100 * (nn1.test.tab[2] + nn1.test.tab[3]) / sum(nn1.test.tab)) [1] 7.68116 ## Example 2 - code based on rattles log script # train artifical neural network >nn2<-nnet(as.numeric(type)-1~., data=spam.train, size=3, decay=0.1, maxit=1000) # predict spam.test dataset on nn2. # ?predict.nnet does have the parameter type='class', but i can't use that here as an option >nn2.pr.test <- predict(nn2, spam.test)...

...p file, however got nothing on that Megh Dal <megh700004@yahoo.com> wrote: Date: Sat, 15 Mar 2008 00:58:02 -0700 (PDT) From: Megh Dal <megh700004@yahoo.com> Subject: Re: How to create following chart for visualizing multivariate time series To: r-help@stat.math.ethz.ch Let me take an artifical matrix : dat = matrix(rnorm(200*200), 200, 200) My goal is to visualize this matrix according to the procedure, described in previous mails. I took Mendelssohn's advice and got following advice : ?plot.im Z <- setcov(owin()) plot(Z) .................... etc However I...

I was wondering if anyone had some sample time series dgp code. I am particularly interested in examples of autoregressive processes and error correction model DGPs. I have attached a more specific example of what I mean. I have tried myself but would hoping someone had some more elegant code that would help me extend my own code. Thanks Luke Keele UNC-Chapel Hill Nuffield College, Oxford

Hi, Is there a way to stack bars in a barchart as well as "beside" bars for the same treatment? eg.... I have one barchart like this: bio<-matrix(c(10,23,9,25),nrow=2,byrow=T) ntreat<-c("n0","n96") colnames(bio)<-ntreat barplot(bio,beside=T) now i want a similar barchart but with stacked bars:

...ichres[.rownum,1]+1, whichres[.rownum,2]) temp <- rbind(rightspot,leftspot,belowspot,abovespot) }) # THIS SETS THE CONSTRUCTED INDICES TO 1 for ( i in 1:length(onespositions) ) { tempbinmat[onespositions[[i]]] <- 1 } #print(tempbinmat) # NOW GET RID OF THE ROWS AND COLUMNS THAT WERE ARTIFICALLY ADDED AT THE START TO MAKE THINGS EASIER tempbinmat <- tempbinmat[2:(nrow(tempbinmat)-1),2:(ncol(tempbinmat)-1)] print(tempbinmat)

Samba experts, I am running samba 3.0.3-5 of Samba on Fedora core 2 (Red Hat). I am trying to use encrypted passwords. However, when a user tries to connect to their samba account, they end up getting automatically disabled. Their encyrpted password becomes all X's. Can anyone tell me why this might be happening at the moment they attempt to login? Here are my global variables in

...n and what can I do to help the situation? I actually would like to just deny the logins from the larger domain from logging in to the Samba ADS domain computers, but perhaps this is not possible with the trust set up between the Win2k3 domains. Is the bottleneck our ldap server, or is there some artifically configured maximum result size coming from a basically default install of openldap? Thanks in advance for any help. Alan