similar to: Help with functions

On Apr 2, 2008, at 4:47 PM, David Greene wrote: >> I think these requirements are not specific for my allocator only, >> but >> are important for checking any register allocator. May be someone >> (Fernando? David? Evan?) who was working on register allocators has >> found a good set of examples covering some of the topics? Or may be >> someone knows good

On Apr 2, 2008, at 9:58 PM, Chris Lattner wrote: > > Could these be added to llvm-test? > > -Chris I just added the two C benchmarks as MultiSource/Benchmarks/NPB-serial. --Owen -------------- next part -------------- A non-text attachment was scrubbed... Name: smime.p7s Type: application/pkcs7-signature Size: 2555 bytes Desc: not available URL:

We use samba purely as for file serving from Solaris 7 or 8 servers. THE PDC and BDCs are NT machines. We have been using Samba 2.0.7, but used 2.2.5 for a new server. One noticeable difference is that smbstatus lists a number of entries "IPC$ nobody nobody" which we did not see in 2.0.7. Is this normal in 2.2.5 or something I should be ivestigating? Eg trout# smbstatus

On Wednesday 02 April 2008 03:06, Roman Levenstein wrote: > Hi, > > As I mentioned some time ago on the mailing list, I'm working on the > implementation of the Sarkar's Extended Linear Scan algorithm for LLVM. Cool! > For testing and debugging of this algorithm, I need some good > test-cases that check different functionalities of the register > allocator, e.g.:

Hello R-users I am new to R and trying to write some functions. I have problems writing functions that takes a data set as an arguement and uses variables in the data. I illustrate my problem with a small example below: sample data #------------------ visual24<-rnorm(30,3,5) visual52<-rt(30,7) dats<- data.frame(cbind(visual24,visual52)) remove(visual24, visual52)

Good day R-Users, I have a problem accessing some values in the output from the summary of an lme fit. The structure of my data is as shown below (I have attached a copy of the full data). id trials endp Z.sas ST 1 1 -1 -1 42.42884 1 1 1 -1 48.12007 2 1 -1 -1 43.42878 2 1 1 -1

Dear R Users I have difficulties accessing the variance components for an lme fit when the variance covariance matrix of the random effects is not positive definite. Can anyone inform me on how to get by this ? Thanks in advance Pryseley --------------------------------- [[alternative HTML version deleted]]

Dear LLVM, Consider two loops with one interation - First with constant lower bound, second with usual non-constant lower bound: int main(int argc, char ** argv) { int numOfIterations= 1; int stride=1; int lowerBound = 1000; - 1st | int lowerBound = argc; - 2nd int upperBound = lowerBound + (numOfIterations - 1)*stride; int i = lowerBound;

On Mon, Feb 27, 2012 at 9:30 AM, Benjamin Kramer <benny.kra at googlemail.com> wrote: > > On 27.02.2012, at 17:13, Николай Лихогруд wrote: > >> Dear LLVM, >> >>     Consider two loops with one interation - >>     First with constant lower bound, second with usual non-constant lower bound: >> >>     int main(int argc, char ** argv) >>     {

Dear R-Users, Is it possible to get the covariance matrix from an lme model that did not converge ? I am doing a simulation which entails fitting linear mixed models, using a "for loop". Within each loop, i generate a new data set and analyze it using a mixed model. The loop stops When the "lme function" does not converge for a simulated dataset. I want to

I am investigating changing BoundsChecking to use address-based rather than size- & offset-based tests. To explain, here is a short code sample cribbed from one of the tests: %mem = tail call i8* @calloc(i64 1, i64 %elements) %memobj = bitcast i8* %mem to i64* %ptr = getelementptr inbounds i64* %memobj, i64 %index %4 = load i64* %ptr, align 8 Currently, the IR for bounds checking

On 27.02.2012, at 18:49, Eli Friedman wrote: > On Mon, Feb 27, 2012 at 9:30 AM, Benjamin Kramer > <benny.kra at googlemail.com> wrote: >> >> On 27.02.2012, at 17:13, Николай Лихогруд wrote: >> >>> Dear LLVM, >>> >>> Consider two loops with one interation - >>> First with constant lower bound, second with usual

On 27.02.2012, at 17:13, Николай Лихогруд wrote: > Dear LLVM, > > Consider two loops with one interation - > First with constant lower bound, second with usual non-constant lower bound: > > int main(int argc, char ** argv) > { > int numOfIterations= 1; > int stride=1; > int lowerBound = 1000; - 1st | int lowerBound =

Hi Kevin, Thanks for your interest and for your deep analysis. Unfortunately, your approach doesn't catch all bugs and is vulnerable to an attack. Consider the following case: ...................... | ----- obj --- | | end ^ ptr ^ ^ end-of-memory The scenario is as follows: - an object is allocated in the last page of the address space - obj is byte

Dear R-Users I used the nlme library to fit a linear mixed model (lme). The random effect standard errors and correlation reported are based on a Log-Cholesky parametrization. Can anyone tell me how to get the Covariance matrix of the random effects, given the above mentioned parameters based on the Log-Cholesky parametrization?? Thanks in advance Pryseley

Hello R-users Is it possible to fit a three level linear mixed effect model in R? If anyone has an idea or sample code, i will appreciate it very much if i can receive it. I am reading the book by Pinheiro and Bates but have not come across that yet! Kind regards Pryseley --------------------------------- [[alternative HTML version deleted]]

Dear R-users Can anyone inform me of a library or more specifically functions that can maximise (or calculate) a Pairwsie likelihood from a data. Better still, i would like to know if there is a function (library) that fits regression models based on pairwise likelihoods. Thanks Pryseley --------------------------------- [[alternative HTML version deleted]]

Dear R users, Can someone inform me about a library/function in R that fits a Plackett-Dale model ? Thanks in advance Pryseley --------------------------------- [[alternative HTML version deleted]]

Nuno, Inspired by this email thread, I spent a bit of time today looking through the implementation of BoundsChecking::instrument(..). Based on my reading of prior work, it should be possible to do these checks in two comparisons, or possibly even one if the right assumptions could be made. Could you provide a bit of background of the expected domains of Size and Offset? In particular,

Dear R-Users I have a problem accessing some values in the output from the summary of an lme fit. I fit the model below: ggg <- lme (ST~ -1 + as.factor(endp):Z.sas + as.factor(endp), data=dat4a, random=~-1 + as.factor(endp) + as.factor(endp):Z.sas|as.factor(trials), correlation = corSymm(form=~1|as.factor(trials)/as.factor(id)), weights=varIdent(form=~1|endp)) hh