similar to: Uniform CD Driver Revision 3.12 Hang in RH 7.2 Install

Hi there, I'm using RH7.2 (with the 2.4.9-30 kernal and it's required components) as a base for a server system running kernel 2.4.18. I've gone to this version to get around non-performing aic7xxx drivers in the stock 7.2 kernels, and updated gigabit ethernet drivers. I have a raid unit (Medea) attached to an Adaptec 3916, coming up as sdb. It has 2kb blocks, but the fault

Hi all, I am REALLY confused with the variance right now. for a discrete uniform distribution on [1,12] the mean is (1+12)/2=6.5 which is ok. y=1:12 mean(y) then var(y) gives me 13 1- on http://en.wikipedia.org/wiki/Uniform_distribution_%28discrete%29 wiki the variance is (12^2-1)/12=143/12 2-

Full_Name: Hsiu-Khuern Tang Version: 2.0.0 OS: Debian GNU/Linux Submission from: (NULL) (156.153.255.236) Hi all, If fit is an rpart object, plot(fit, uniform=TRUE) ignores uniform=TRUE if the graphics device is not already open. To reproduce this: library("rpart") example(plot.rpart) dev.off() # <- works OK without this plot(fit, uniform=TRUE) # <- uniform=TRUE is ignored

> What specific information do you need? All my experiments eventually analyze or transform the data dependence graph, which takes into account both registers and aliasing information. Because there is already a built-in support for graphs, I hoped there is also built-in support for data dependence graphs. That would allow, for example, to display them naturally with dot utility.

> Is there some uniform way to find data dependence between two (sets of) > instructions? There is use/def data, and there is AliasAnalysis > information, but I am looking for something more high-level and uniform. > I guess I am missing then was abandoned. Any ideas? Nope, there currently isn't a uniform interface for doing so, at least not at the LLVM level. As you said,

In the wake of discussions about documentation of R's random number generation (and whether there should be links to .Random.seed in other places), here is a proposed patch to Uniform.Rd from 2.8.1. (Watch out for line breaks.) [R-core may choose not to accept this, but at least it's easy if they want it.] cheers Ben Bolker *** Uniform.Rd.orig 2009-01-04 14:10:47.000000000 -0500

When `length( skewed.probs ) > 200' uniform samples are generated in R-devel. R-3.5.1 behaves as expected. `epsilon` can be a lot bigger than illustrated and still the uniform distribution is produced. Chuck > set.seed(123) > > epsilon <- 1e-10 > > ## uniform to 200 then small > p200 <- prop.table( rep( c(1, epsilon), c(200, 999-200))) > ## uniform to 201

Guys, Is there some uniform way to find data dependence between two (sets of) instructions? There is use/def data, and there is AliasAnalysis information, but I am looking for something more high-level and uniform. I guess I am missing something, because such interface seems to exist once and then was abandoned. Any ideas? Victor -- IBM Research Lab in Haifa, Israel

Hi I have generate two variable with uniform disrtribution A and B and I want to calculate the number of cases where B<A^2 So I done it by sum(B<A^2), but I'm not sure if there is anther way to calculate the number of cases in the distribution or what I have done is fine thanks for helping me Woo -- View this message in context:

Hi all, Some brain-storming on an issue with SPMD/SIMT backend support where I think some additional IR attributes would be useful. Sorry for the somewhat long mail; the short version of my current thinking is that I would like to have the following: 1) convergent: a call to a function with this attribute cannot be moved to have additional control dependencies; i.e., moving it from A to B is

On Tue, Dec 20, 2016 at 11:00:09AM +0800, Ruiling Song wrote: > Hi, > > I am working on a new LLVM target for Intel GPU, which also has same kind > of scalar/vector register classes used in AMDGPU target. Like for a i32 > virtual register, it will be held in scalar register if its value is > uniform across a wavefront/warp, otherwise it will be in a vector register. > Does

I have a vector with lots of different numbers. I need to make a graph showing the Uniform Distribution of the figures. I have created a graph showing all the different values, but now want individual Gaussian Kernel round each point. This is what I have but each time it comes up with an error as I have just based it on the Normal Distribution, but I'm not sure what I need to change to make it

Hi I have the table data below Simula.Capital<-data.frame(Week=c(0:52), Production=0) Simula.Capital$Production<-round(c(120,rnorm(52, mean = 100, sd = 25)), 0) weeks=3 i<-1; Sell<-NULL; Maximo<-NULL for(i in seq(along= Simula.Capital$Production)){ Maximo<-Simula.Capital[2,"Production"] Sell<-c(Sell,(round(runif(weeks, min=0, max=Maximo), 0))) i=i+1 }

Hello, This might be a bit off-topic, and I apologize. But I think this might be a FS problem I'm having with ext3 (but I'm not sure). I finally got RH 7.2 to install on my new box. The box looks like: Epox 8KHA+ motherboard (KT 266A chipset) 512 Meg DDR DRAM WD 200BB HD S3 Savage 2000 64 Meg card (set at AGP 4x) NEC 5FGp monitor Intel NIC CD floppy I partitioned the HD to look

On 24.10.2016 21:54, Mehdi Amini wrote: >> On Oct 24, 2016, at 12:38 PM, Nicolai Hähnle via llvm-dev <llvm-dev at lists.llvm.org> wrote: >> Some brain-storming on an issue with SPMD/SIMT backend support where I think some additional IR attributes would be useful. Sorry for the somewhat long mail; the short version of my current thinking is that I would like to have the following:

Hi all. I am having a problem using color2D.matplot I am trying to visualize several different matrices under two color ranges. One color range corresponds to values less than 1 and the second color range for values greater than 1. However, the minimum value of each matrix differs and is automatically set to have the smallest value in the color range. Similarly, the maximum value of each matrix

(I work on CUDA / PTX.) For one thing I'm in favor of having fewer annotations rather than more, so if we can do this in a reasonable way without introducing the notion of co-convergent calls, I think that would be a win. The one convergent annotation is difficult enough for the GPU folks to grok and then keep in cache, and everyone who works on llvm has to pay the cost of keeping their

Hello, What is the best way to get ranks for a vector of values, limit the range of rank values and create equal count in each group? I call this uniform ranking...uniform count/number in each group. Here is an example using three groups: Say I have values: x = c(3, 2, -3, 1, 0, 5, 10, 30, -1, 4) names(x) = letters[1:10] > x a b c d e f g h i j 3 2 -3 1 0 5 10 30 -1 4 I

Hi all, This is my welcome message to everyone in the group, Chaw! I've recently installed Redhat 7.2 with its companion Samba installtion, if samba version is so important in this case please advise. I've set up the standard smb.conf to contain the following [global] workgroup = myhome interfaces = 192.168.1.1/24 hosts allow = 192.168.1 127. [homes] comment = My home

Hello, I would like to uniformly distribute values from 0 to 200. Can someone help me find the appropriate uniform distribution generator? I would like to thank you in advance for your help. Best Regards Alex [[alternative HTML version deleted]]