similar to: lm and levels

We have a test that looks like this… define void @array16_store() { ; CHECK-LABEL: array16_store: ; CHECK: ldi [[REG1:r[0-9]+]], 204 ; CHECK: ldi [[REG2:r[0-9]+]], 170 ; CHECK: sts int.array+3, [[REG2]] ; CHECK: sts int.array+2, [[REG1]] ; CHECK: ldi [[REG1:r[0-9]+]], 187 ; CHECK: ldi [[REG2:r[0-9]+]], 170 ; CHECK: sts int.array+1, [[REG2]] ; CHECK: sts int.array, [[REG1]] ; CHECK: ldi

Hi all, In several of our tests, I have noticed that the register allocator allocates to virtual registers in a different order when compiling with the clang option -g. Before entering the register allocator, the code is identical when compiling with and without -g (with the exception of " DBG_VALUE" instructions). The only difference I can see is the value assigned to the slot index

Hi list, in our LLVM-based-project we are writing a backend for our processor. The architecture is a quite straight-forward RISC, but it does not have hardware interlocks, i.e. data hazards involving memory access must be resolved by the compiler, either by scheduling unrelated instructions or by inserting NOOPs into the load delay slots: ---- For example, code which looks like that: load

On Jan 19, 2009, at 9:17 AM, Patrick Boettcher wrote: > Hi list, > > in our LLVM-based-project we are writing a backend for our > processor. The > architecture is a quite straight-forward RISC, but it does not have > hardware interlocks, i.e. data hazards involving memory access must be > resolved by the compiler, either by scheduling unrelated > instructions or >

Hi All, I am interested in creating a function that will take x number of lm objects and automate the comparison of each model (using anova). Here is a simple example (the actual function will involve more than what Im presenting but is irrelevant for the example): # sample data: id<-rep(1:20) n<-c(10,20,13,22,28,12,12,36,19,12,36,75,33,121,37,14,40,16,14,20)

I’m not sure if it’s truly deterministic. It always gives the same results (so far) on my machine but I’m not sure that’s enough. My guess is it’s probably going to be deterministic on one machine but might well not be deterministic across environments. Like it might give varying results if cross compiled on different hosts, macOS vs intel Linux vs arm vs s390. (Obviously AVR is always a cross

> On Feb 2, 2017, at 8:20 AM, David Blaikie <dblaikie at gmail.com> wrote: > > The goal/intent is that debug info does not affect code generation. There are (many?) bugs. I think Apple folks (cc'd Adrian) may be looking at this a bit recently, not sure. > > The fixes aren't usually too invasive (usually involve something counting instructions where it needs to skip

> On Feb 2, 2017, at 8:20 AM, David Blaikie via llvm-dev <llvm-dev at lists.llvm.org> wrote: > > The goal/intent is that debug info does not affect code generation. There are (many?) bugs. I think Apple folks (cc'd Adrian) may be looking at this a bit recently, not sure. > > The fixes aren't usually too invasive (usually involve something counting instructions where

Hello, I'm writing a backend for an architecture that only has LOAD Instructions that first copy the old value of the target register in another register and after that load the provided value into the register. Example of an addition: load a, reg1; // -> copies old value of reg1 in reg2 and loads value from a into reg1 load b, reg1; // -> copies old value of reg1 in reg2 and loads

Each of the data sets contains monthly observations on price indices for 7 countries. I use the fitted values from reg1 in the reg2 model. The interior loop executes without error as long as I explicitly specify the data set, i.e. data=dat70. However the code fails to execute if I specify the model in the form of the commented line, i. e reg1 <-dynlm(form1,data=Dnames[j]) I get the following

Hi,   A question on something which normally should be easy !   I perform a linear regression using lm function:   > reg1 <- lm (a b+c+d, data = database1)   Then I try to perform the Chow (1960) test (structural change test) on my regression. I know the breakpoint date. I try the following code like it is described in the “Examples” section of the “strucchange” package :   > sctest(reg1,

To generate code for a target which doesn't have immediate constant as instruction operand, do I (the target specific back-end, XXXTarget) need to provide code to break up the SDNode with constant (like ISD::ADD $reg1, #1) to 2 SDNodes (ISD::LOAD $reg2, #1; ISD::ADD $reg1, $reg2) in my XXXTargetLowering::LowerOperation, or LLVM target-independent framework can do such transformation

Dear R Users, I am looking for an alternative to AIC or BIC to choose model parameters. This is somewhat of a general statistics question, but I ask it in this forum as I am looking for a R solution. Suppose I have one dependent variable, y, and two independent variables, x1 an x2. I can perform three regressions: reg1: y~x1 reg2: y~x2 reg3: y~x1+x2 The AIC of reg1 is 2000, reg2 is

Hi all, Our target does not have native support for 64-bit integers, so we rely on library calls for certain operations (like sdiv). We recently ran into a problem where these operations that are expanded to library calls aren't maintaining the proper ordering in relation to other chains in the DAG. The following snippet of a DAG demonstrates the problem. t0: ch = EntryToken t2:

Hi folks, Warning: I don't know if the result I am getting makes sense, so this may be a statistics question. The fitted values from my binomial lmer mixed model seem to consistently overestimate the cell means, and I don't know why. I assume I am doing something stupid. Below I include code, and a binary image of the data is available at this link:

I am relatively new to R and object oriented programming. I have relied on SAS for most of my data analysis. I teach an introductory undergraduate forecasting course using the Diebold text and I am considering using R in addition to SAS and Eviews in the course. I work primarily with univariate or multivariate time series data. I am having a great deal of difficulty understanding and working with

I have a puzzle.... When I include an interaction in the model, many predicted probabilities are above 1. Is that a problem with my model? I thought the predicted prob can't be bigger than 1... Any help would be really appreciated! Thanks! K. reg1<-glm(pyea~male+edu+married+inc+relig+factor(time)+ factor(time)*male, data=mydata, family=binomial(link="logit"))

Hi, I am trying to create a scatterplot, coding each point to one of 5 populations. I was successful when I did this for one set of data, yet when I try plotting other data a blank plot appears (although the axes are labelled and I can fit the regression lines from each population). I have tried a variety of things to fix this but nothing seems to work. I can plot the points if I do not

Hello, This is a question motivated by curiosity, not a pressing problem. Any responses are much appreciated! In the following code, function reg1 calls glm with a formula object while reg2 uses a string. In both cases, glm works; however, in the second case, the add1 function fails with the following message: "Error in eval(predvars, data, env) : invalid 'envir' argument."

Hello~   Did any one have used the package 'orcutt' ?   I find that it can not work smoothly in a single variable regression. I use the example following, it function very well.   But when I regress "cons" on "price" (use the "reg1<-lm(cons~price+income+temp)") , then  use "reg11<-cochrane.orcutt(reg1) ". There is an error message “Error in