similar to: [LLVMdev] dead store elimination with external functions

Hi Peng, > Here is an llvm ir of a module. It seems that "b1" should be eliminated by dead > store elimination. But it did not. > > Could anyone explain why and how "b1" can be eliminated? > > Thanks, > -Peng > > %0 = type { [3 x [1 x i32]] } > > declare %0 @external_fcn3(i32, i32, i32, i32, [3 x [1 x i32]]*) > > define i32 @f3(i32 %a)

Maybe I should say b2 should be eliminated. Since b1 and b2 are same type and b1 is not used any more after the function call, b2 can be replaced by b1. This will save an alloca. In my test, if the function is not external, b1 is eliminated. But with external function, it did not. Since the ir is generated, I cannot manually update b2 with b1. I would like the optim pass do it. On

(sorry if this is a duplicate-problems with posting at my end) ---- Hello all, I need some help with a nice R-idiomatic and efficient solution to a small problem. Essentially, I am trying to eliminate randomly half of the entries in a vector that contains index values into some other vectors. More details: I am working with two strings/vectors of 0s and 1s. These will contain about 200

Hi, I run the following commands. 'A' has 3 levels and 'B' has 4 levels. Should there be totally 3+4 = 7 coefficients (A1, A2, A3, B1, B2, B3, B4)? > a=3 > b=4 > n=1000 > A = rep(sapply(1:a,function(x){rep(x,n)}),b) > B = as.vector(sapply(sapply(1:b, function(x){rep(x,n)}), function(x){rep(x,a)})) > Y = A + B + rnorm(a*b*n) > > fr =

Hi all, I am a biological student and need your help in statistics. I have two sets of binomial distributed numbers: {a1, a2, ..., an} and {b1, b2, ..., bn}. How can I get the distribution of the ratios of the two sets of numbers {a1/b1, a2/b2, ..., an/bn}? Is there a formula to transform the distributions? Or where I can start if I want to learn necessary techniques? Thank you a lot! xiechao

Hi I searched in the list and only found questions without answers e.g. http://finzi.psych.upenn.edu/R/Rhelp02a/archive/19955.html : Is there a way to get the same results with lme as with aov with Error()? Can anybody reproduce the following results with lme: id<-c(1,1,1,2,2,2,3,3,3,4,4,4,5,5,5,1,1,1,2,2,2,3,3,3,4,4,4,5,5,5,1,1,1,2,2,2,3,3,3,4,4,4,5,5,5)

For an application, I have formulas defined in terms of a left Kronecker product of matrices, A,B, meaning A \otimes_L B = {A * B[i,j]} -- matrix on the left multiplies each element on the right. The standard kronecker() function is the right Kronecker product, A \otimes_R B = {A[i,j] * B} -- matrix on the right multiplies each element on the left. The example below shows the result of

I've two data.tables as shown below: *** N = 10 A.DT <- data.table(a1 = c(rnorm(N,0,1)), a2 = NA)) B.DT <- data.table(b1 = c(rnorm(N,0,1)), b2 = 1:N) setkey(A.DT,a1) setkey(B.DT,b1) *** I tried to change my previous data.frame implementation to a data.table implementation by changing the for-loop as shown below: *** for (i in 1:nrow(B.DT)) { for (j in nrow(A.DT):1) { if

Hi, I don't understand why this code doesn't work (f(b2)): ///////////////// setClass("B0", representation(b0 = "numeric")) setClass("B1", representation("B0", b1 = "character")) setClass("B2", representation("B1", b2 = "logical")) f <- function(x) class(x) setMethod("f", "B0",

Hello everyone! I am having some trouble supplying the gradient function to nlm in R for windows version 2.2.1. What follows are the R-code I use: fredcs39<-function(a1,b1,b2,x){return(a1+exp(b1+b2*x))} loglikcs39<-function(theta,len){ value<-sum(mcs39[1:len]*fredcs39(theta[1],theta[2],theta[3],c(8:(7+len))) - pcs39[1:len] * log(fredcs39(theta[1],theta[2],theta[3],c(8:(7+len)))))

Dear Wiza[R]ds, I am very grateful to Duncan Murdoch for his assistance with this problem. His help was invaluable. However, the problem has become a little more complicated for me. Now, in each plot, I need to plot the observed and fitted values of a supine and upright posture experiment. Here is what I have and how far I got. # tritiated (3H)-Norepinephrine(NE) disappearance from plasma #

Hello, I am new to R and I am trying to estimate a discrete model with three choices. I am stuck at a point and cannot find a solution. I have probability functions for occurrence of these choices, and then I build the likelihood functions associated to these choices and finally I build the general log-likelihood function. There are four parameters in the model, three of them are associated to

Dear all, I'm struggling to find the best (set of?) function(s) to do repeated measures logistic regression on some data from a psychology experiment. An artificial version of the data I've got is as follows. Firstly, each participant filled in a questionnaire, the result of which is a score. > questionnaire ID Score 1 1 6 2 2 5 3 3 6 4 4 2 ...

Hi Everyone, I'm having trouble figuring out a compile failure on ppc64le. The failure is at https://travis-ci.org/noloader/cryptopp-autotools/jobs/602187190 . The message is: /bin/bash ./libtool --tag=CXX --mode=compile clang++ -DHAVE_CONFIG_H -I. -DCRYPTOPP_DISABLE_POWER8 -pipe -mcpu=power7 -mvsx -maltivec -g -O2 -MT libppc_power7_la-ppc_power7.lo -MD -MP -MF

Dear Wiz[R]ds, I am deeply grateful for the help from Duncan Murdoch, Gray Calhoun, and others. We are almost there. For whatever reason, I can't change the symbol from a circle to a triangle in the upright posture plots. Any ideas? I have included the problem in full. # tritiated (3H)-Norepinephrine(NE) disappearance from plasma # concentrations supine and upright # supine datasu <-

below is a code to compute hessian matrix , which i need to generate 29 number of different matrices for example first element in x1 and x2 is use to generate let say matrix (M1) and second element in x1 and x2 give matrix (M2) upto  matrix (M29) corresponding to the total number of observations and b1 and b2 are constant.  can some one guide me or help to implement this please. I did not

I'm sure this must be a nls() newbie question, but I'm stumped. I'm trying to do the example from Draper and Yang (1997). They give this snippet of S-Plus code: Specify the weight function: weight < - function(y,x1,x2,b0,b1,b2) { pred <- b0+b1*x1 + b2*x2 parms <- abs(b1*b2)^(1/3) (y-pred)/parms } Fit the model gmfit < -nls(~weight(y,x1,x2,b0,b1,b2),

Dear R users: I have some difficulties analizing data with mixed effects NLME and the last version of R. More concretely, I have a repeated measures design with a single group and 2 experimental factors (say A and B) and my interest is to compare additive and nonadditive models. suj rv A B 1 s1 4 a1 b1 2 s1 5 a1 b2 3 s1 7 a1 b3 4 s1 1 a2

I'm solving 4 complex equations simultaneously. Code is below. The code returns only zero's for the solution though there should also be a non-zero result. I'm pretty confident that the equations are correct because they are straight from a published paper and I checked them pretty thoroughly. The parameter values I used are from the published paper as well. Any suggestions for how

Full_Name: Soren Feodor Nielsen Version: 2.5.0 OS: linux-gnu Submission from: (NULL) (130.225.103.21) The print-out of xtable in the following example is wrong; instead of yielding the correct ci's for the second model it repeats the ci's from the first model. require(xtable) require(MASS) data(cats) b1<-lm(Hwt~Sex,cats) b2<-lm(Hwt~Sex+Bwt,cats)