search for: conding

Here is a working snippet. library(epitools) mat <- matrix(c(10,15,60,25,98, 12,10,70,28,14, 9,11,68,10,12 ,8,13,20,11,58) ,ncol=2) colnames(mat) <- c("treatmentA","treatmentB") row.names(mat) <- paste("Cond",rep(1:10,1)) dimnames(mat) <- list("Condition" = row.names(mat), "instrument" = colnames(mat)) > mat instrument

Here is a working snippet. library(epitools) mat <- matrix(c(10,15,60,25,98, 12,10,70,28,14, 9,11,68,10,12 ,8,13,20,11,58) ,ncol=2) colnames(mat) <- c("treatmentA","treatmentB") row.names(mat) <- paste("Cond",rep(1:10,1)) dimnames(mat) <- list("Condition" = row.names(mat), "instrument" = colnames(mat)) > mat instrument

Here is a working snippet. library(epitools) mat <- matrix(c(10,15,60,25,98, 12,10,70,28,14, 9,11,68,10,12 ,8,13,20,11,58) ,ncol=2) colnames(mat) <- c("treatmentA","treatmentB") row.names(mat) <- paste("Cond",rep(1:10,1)) dimnames(mat) <- list("Condition" = row.names(mat), "instrument" = colnames(mat)) > mat instrument

On 08/16/2013 02:42 AM, Star Tan wrote: > At 2013-08-16 12:44:02,"Tobias Grosser" <tobias at grosser.es> wrote: >> Hi, >> >> I tried to reproduce your findings, but could not do so. > > > Sorry, I did not put all code in my previous email because the code seems a little too long and complicated. > You can refer to the detailed C code and LLVM IR

Hi everyone, I'm having some trouble with my ifelse statements. I'm trying to put 12 conditions within 3 groups. Here is the code I have so far: dat$cond <- ifelse(test = dat$cond == "cond1" | dat$cond == "cond2" | dat$cond == "cond3" dat$cond == "cond4" yes = "Uniform" no = ifelse(test =

At 2013-08-16 22:32:30,"Tobias Grosser" <tobias at grosser.es> wrote: >> >> Yes, I have changed the original code to the form you suggested: >> for (i >> for (j >> ... >> x=1 > >Sorry, I meant > x[0] += > It is interesting that Polly would run much faster if we change the

On Wed, Sep 2, 2015 at 5:36 AM, James Molloy <james at jamesmolloy.co.uk> wrote: > Hi, > > Coremark really isn't a good enough test - have you run the LLVM test suite > with this patch, and what were the performance differences? For the test suite single source benches, the 235 tests improved performance, 2 regressed and 705 were unchanged. That seems very optimistic.

On Mon, Dec 18, 2017 at 5:46 PM Xinliang David Li <davidxl at google.com> wrote: > The introduction of cleanup.cond block in b.ll without loop-rotation > already makes the layout worse than a.ll. > > > Without introducing cleanup.cond block, the layout out is > > entry->while.cond -> while.body->ret > > All the arrows are hot fall through edges which is

Dear R community, I am trying to resolve a discrepancy between the way SPSS and R handle 2-way, repeated measures ANOVA. An experiment was performed in which samples were drawn before and after treatment of four groups of subjects (control and disease states 1, 2 and 3). Each group contained five subjects. An experimental measurement was performed on each sample to yield a

Hi, I am investigating a performance degradation between llvm-3.1 and llvm-3.2 (Note: current top-of-tree shows a similar degradation) One issue I see is the following: - 'loop invariant code motion' seems to be depending on the result of the 'reassociate expression' pass: In the samples below I observer the following behavior: Both start with the same expression: %add = add

Hi! I'm having some trouble with a pass I'm writing. I'm using DemotePHIToStack to remove all phi node in my code with this code (this is the first thing I do in my pass): // Erase phi node vector<PHINode*> phis; for (Function::iterator i=f->begin();i!=f->end();++i) { for(BasicBlock::iterator b=i->begin();b!=i->end();++b) {

In your original file, %6 is defined in if.end11 and is used in cond.end. if.end11 branches to cond.true and cond.false, both of which branch unconditionally to cond.end. Therefore %6 dominates its use. In your second file %18 is defined in end.11 and used in cond.end. However, end.11 no longer dominates cond.end because you have rewritten all branches to go through the switch statement in

Thanks to David's help I subset my large data set and produced a smaller one for a single stream and 7 factors of interest. The structure of this data frame is: str(burns.tds.anal) 'data.frame': 718 obs. of 4 variables: $ site : Factor w/ 143 levels "BC-0.5","BC-1",..: 1 1 4 6 4 4 4 5 5 5 $ sampdate: Date, format: "1996-06-02"

Hi, Recently 10% performance regression on an important benchmark showed up after we integrated https://reviews.llvm.org/rL318299. The analysis showed that rL318299 triggered loop rotation on an multi exits loop, and the loop rotation introduced code layout issue. The performance regression is a side-effect of rL318299. I got two testcases a.ll and b.ll attached to illustrate the problem. a.ll

Hi, I have come across a couple of cases where the code generated after CodeGenPrepare pass has "br i1 false .." with both true and false conditions preserved and this propagates further and remains the same in the final assembly code/executable. In CodeGenPrepare::runOnFunction, ConstantFoldTerminator (which handles the br i1 false condition) is called only once and if after the

As far as I can understand of the code, the Reassociate tries to achieve this result by its "ranking" mechanism. If it dose not, it is not hard to achieve this result, just restructure the expression in a way such that the earlier definition of the sub-expression is permute earlier in the resulting expr. e.g. outer-loop1 x= outer-loop2 y =

It's an interesting problem. The best stuff I've seen published is by Cooper, Eckhart, & Kennedy, in PACT '08. Cooper gives a nice intro in one of his lectures: http://www.cs.rice.edu/~keith/512/2012/Lectures/26ReassocII-1up.pdf I can't tell, quickly, what's going on in Reassociate; as usual, the documentation resolutely avoids giving any credit for the ideas. Why is that?

Dear R-users I have 5 dependent variables (y1 to y5) and one independent variable (x) and 3 conditioning variables (m, n, and 0). Each of the conditioning variables has 2 levels. I created 2*4 panel plots. xyplot(y1+y2+y3+y4+y5 ~ x | m*n*o,layout = c(4,2)) I would like to reorder the 8 panels. I tried to use index.cond (e.g., index.cond = list(c(1,3,2,4,5,7,6,8)) but it didn't work out.

Esteemed UseRs, This must be embarrassingly trivial to achieve with e.g., melt() and cast(): deduplicating records ("pw.X" in example) for a given set of responses ("cond.Y" in example). Hopefully the runnable example shows clearly what i have and what i'm trying to convert it to. But i'm just not getting it, ?cast that is! So i'd really appreciate some ones

At 2013-08-16 12:44:02,"Tobias Grosser" <tobias at grosser.es> wrote: >Hi, > >I tried to reproduce your findings, but could not do so. Sorry, I did not put all code in my previous email because the code seems a little too long and complicated. You can refer to the detailed C code and LLVM IR code on http://llvm.org/bugs/show_bug.cgi?id=16843 There are four attachments