similar to: Predicting x from y

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, Is there a kind of scope mechanism in the instruction lowering pattern language in order to control where instructions are inserted or how they are later reordered during the SelectionDiag linearization? I know the glue chain that stick instructions together. But such mechanism in not provided in instruction lowering pattern. I'm facing many situations where some patterns are lowered into

I've tried without success to calculate interaction and main effects contrasts using R. I've found the functions C(), contrasts(), se.contrasts() and fit.contrasts() in package gmodels. Given the url for a small dataset and the two-way anova model below, I'd like to reproduce the results from appended SAS code. Thanks. --Dale. ## the dataset (from Montgomery) twoway <-

im using the most recent version of ubuntu and i wanted to run WOW but i have been unable to even configure it due to a lack of text on the configure screen. when i change tabs there is a sudden flash of jumbled texts that immediately subsides. and i am using nvdia drivers for my graphics card. here is what it looks like (title bar got cut off) [Image:

The DAG dumping will try to print some of the nodes "inline" (i.e. where they are used) to make the output more readable, so the dump of the DAG may not strictly reflect the node ordering. -Krzysztof On 5/4/2018 8:18 AM, Dominique Torette via llvm-dev wrote: > Here is a last example to illustrate my concern. > > The problem is about the lowering of node t13. > >

Here is a last example to illustrate my concern. The problem is about the lowering of node t13. Initial selection DAG: BB#0 '_start:entry' SelectionDAG has 44 nodes: t11: i16 = Constant<0> t0: ch = EntryToken t3: ch = llvm.clp.set.rspa t0, TargetConstant:i16<392>, Constant:i32<64> t5: ch = llvm.clp.set.rspb t3,

Hello, I've hit an assertion in SelectionDAG where we try to merge 2 loads that have the same operands but their MMO flags differ. One is dereferenceable and one is not. I'm not sure what the underlying issue here is: 1) MDSDNode with the same operands should have the same flags set on their respective MMO. The fact the flags differ when the opcode,types,operands and address-space are

Sanjay, I'm looking at some missed optimizations caused by D70246. Here's a test case: define <4 x float> @f(i32 %t32, <4 x float>* %t24) { .entry: %t43 = insertelement <3 x i32> undef, i32 %t32, i32 2 %t44 = bitcast <3 x i32> %t43 to <3 x float> %t45 = shufflevector <3 x float> %t44, <3 x float> undef, <4 x i32> <i32 0, i32 undef,

Krzysztof, Thanks for your interest to my questions. In order to clarify the context, here is the C source file of my test case. The 3 builtins initialize some stack pointers. They have to be executed before any other instruction. extern float fdivfaddfmul_a(float a, float b, float c, float d); volatile static float x1,x2,x3,x4; void _start(void) { float res;

Hello. I'm having problems at instruction selection with my back end with the following basic-block due to a vector add with immediate constant vector (obtained by vectorizing a simple C program doing vector sum map): vector.ph: ; preds = %vector.memcheck50 %.splatinsert = insertelement <8 x i64> undef, i64 %i.07.unr, i32 0

Hi, we would like to establish a small Lustre instance and for the OST planning to use standard Dell PE1950 servers (2x QuadCore + 16 GB Ram) and for the disk a JBOD (MD1000) steered by the PE1950 internal Raid controller (Raid-6). Any experience (good or bad) with such a config ? thanxs, Martin

What models? Jeff Noxon (jeff-asterisk at planetfall.com) wrote*: > >I just purchased a bunch of Nortel Meridian POTS phones that support >intercom on the 3rd pair. I intend to get it working with Asterisk. >The phones support MWI, have a 3-line display, callerID, call waiting >callerID, 2 lines...very nice. > >On Thu, Feb 27, 2003 at 01:07:19AM -1000, James H. Thompson

Dear List, I have fitted a spherical function to my variogram using "variofit(...)" from GeoR. Now I would like to predict some data with the function "predict(object,...)" from package stats. Does anyone know wether this works and if it does how to do it? Thanks a lot! Anna

Basically all I want to do is predict a scalar response using some curves. I've got as far as doing a regression (using fRegress from the fda package) but have no idea how to apply the results to a NEW set of curves (for prediction). I have N=536 curves, and 536 scalar responses. Here's what I've done so far: * I've created a basis for the curves. * I've created a

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 What are the best tools (models within R) for use in predicting stock market trading values? Currently I'm using the smooth.spline but are there any others used in practice? Do I find the mathematical formulations of R-functions somewhere (I now I could deduct it from the source code algorithms but ...)? Of the smooth.spline() for example? Thank

Could anybody help me out please? > cl<-as.factor(traindata[,13]) > knn(traindata[1:295,2], newdata[1:32,2], cl,k=2, prob=TRUE) Error in knn(traindata[1:295, 2], newdata[1:32, 2], cl, k = m, prob = TRUE) : Dims of test and train differ Both traindata and newdata have 13 elements. Only one of the first 12 elemnets is needed to predict the 13 element. What's the problem of

Dear listers, I experinced a problem prdicting the values using the LME with multilevel data. I have NA's in my dependent variable and the model is fitted only on the completed cases. I want to estimate the predicted values for the rest of the data (those cases with missing dep. variable) I extracted a subset from the original file containing the variables used in the model as well as the

Hello together, I would like to predict my fitted values on a new dataset. The original dataset consists of the variable a and b (data.frame(a,b)). The dataset for prediction consists of the same variables, but variable b has a constant value (x) added towards it (data.frame (a,b+x). The prediction command returns the identical set of predicted values as for the original dataset yet I would have

Hi, I noticed the following fortune in R 2.7 and 2.6.2: fortune('Spreads') If anything, there should be a Law: Thou Shalt Not Even Think Of Producing A Graph That Looks Like Anything From A Spreadsheet. -- Ted Harding (in a discussion about producing graphics) R-help (August 2008) Just wondering, what function and library gave this detailed prediction? Cheers

Lets say I have a linear model and I want to find the average expented value of the dependent variable. So let's assume that I'm studying the price I pay for coffee. Price = B0 + B1(weather) + B2(gender) + ... What I'm trying to find is the predicted price for every possible combination of values in the independent variables. So Expected price when: weather=1, gender=male weather=1,