search for: m5

...17, at 4:01 AM, Jim Lemon <drjimlemon at gmail.com> wrote: > > Hi Bogdan, > Kinda messy, but: > > N <- data.frame(N=c("n1","n2","n3","n4")) > M <- data.frame(M=c("m1","m2","m3","m4","m5")) > C <- data.frame(n=c("n1","n2","n3"), m=c("m1","m1","m3"), I=c(100,300,400)) > MN<-as.data.frame(matrix(NA,nrow=length(N[,1]),ncol=length(M[,1]))) > names(MN)<-M[,1] > rownames(MN)<-N[,1] > C[,1]<-as....

Dear Bert, thank you for your response. here it is the piece of R code : given 3 data frames below --- N <- data.frame(N=c("n1","n2","n3","n4")) M <- data.frame(M=c("m1","m2","m3","m4","m5")) C <- data.frame(n=c("n1","n2","n3"), m=c("m1","m1","m3"), I=c(100,300,400)) how shall I integrate N, and M, and C in such a way that at the end we have a data frame with : - list N as the columns names - list M as the...

...or your answers and suggestions! Well, I want to do some experiments about the compiler optimization in llvm under SPM and I need performance evaluation. As I know, alpha is a general-purpose CPU, so I consider that arm may be an alternative. Simplesim-arm is able to do performance simulation while m5-arm is still under development. Then, what should I do? Do the experiment under m5-alpha or use simplesim-arm? And if I choose to use m5-alpha, can I use llvm as the compiler for this simulator? 2008-06-17 左琦 发件人： David Greene 发送时间： 2008-06-17 00:36:59 收件人： llvmdev at cs.uiuc.edu 抄送：...

hello everyone, I'm wondering whether llvm can work together with simplescalar. Would anyone please give me some clarification? Thanks a lot!

hello everyone, I'm wondering whether llvm can work together with simplescalar. Would anyone please give me some clarification? Thanks a lot!

Hi Bogdan, Kinda messy, but: N <- data.frame(N=c("n1","n2","n3","n4")) M <- data.frame(M=c("m1","m2","m3","m4","m5")) C <- data.frame(n=c("n1","n2","n3"), m=c("m1","m1","m3"), I=c(100,300,400)) MN<-as.data.frame(matrix(NA,nrow=length(N[,1]),ncol=length(M[,1]))) names(MN)<-M[,1] rownames(MN)<-N[,1] C[,1]<-as.character(C[,1]) C[,2]&lt...

...t;drjimlemon at gmail.com> wrote: > > > > Hi Bogdan, > > Kinda messy, but: > > > > N <- data.frame(N=c("n1","n2","n3","n4")) > > M <- data.frame(M=c("m1","m2","m3","m4","m5")) > > C <- data.frame(n=c("n1","n2","n3"), m=c("m1","m1","m3"), > I=c(100,300,400)) > > MN<-as.data.frame(matrix(NA,nrow=length(N[,1]),ncol=length(M[,1]))) > > names(MN)<-M[,1] > > rownames(MN)<...

...m> wrote: >> > >> > Hi Bogdan, >> > Kinda messy, but: >> > >> > N <- data.frame(N=c("n1","n2","n3","n4")) >> > M <- data.frame(M=c("m1","m2","m3","m4","m5")) >> > C <- data.frame(n=c("n1","n2","n3"), m=c("m1","m1","m3"), >> I=c(100,300,400)) >> > MN<-as.data.frame(matrix(NA,nrow=length(N[,1]),ncol=length(M[,1]))) >> > names(MN)<-M[,1] >> &...

#I am trying to understand how R fits models for contrasts in a #simple one-way anova. This is an example, I am not stupid enough to want #to simultaneously apply all of these contrasts to real data. With a few #exceptions, the tests that I would compute by hand (or by other software) #will give the same t or F statistics. It is the contrast estimates that R produces #that I can't seem to

Hi all, I am new to LLVM (even the field of compiler) and currently I am engaged in the work of adapting LLVM IR to M5 simulator to observe the enhancement of the novel architecture we design. Simply speaking if you know little about M5, my aim is to know how LLVM IR is interpreted and encoded, then try to implement it in the framework of M5. I have read the LLVM documents, yet I still have some questions as follow...

...m2 <- scale(m1) # find cholesky decomp c1 <- chol(var(m2)) # force to be independent m3 <- m2 %*% solve(c1) # create new correlation matrix: cor2 <- cbind( rbind( cor1, z=c(.5,.3,.1,.05) ), z=c(.5,.3,.1,.05,1) ) # create new matrix m4 <- m3 %*% chol(cor2) # uncenter and unscale m5 <- sweep( m4, 2, attr(m2, 'scaled:scale'), '*') m5 <- sweep( m5, 2, attr(m2, 'scaled:center'), '+') ##Check they are equal zapsmall(cor(m5))==zapsmall(cor2) Thanks, ben

This email is also sent to cluster-devel at redhat.com. Since this issue is about both dlm and ocfs2, I send the email here to look for help from upstream. This is an already known issue. on ocfs2 with user space cluster stack, run the test script from http://people.redhat.com/~teigland/make_panic on the mounted ocfs2 volume from 2 nodes simultaneously, the access to ocfs2 volume on both nodes

...od Morning Im busy doing an exercise on Foundation of Statistical Description and Analysis,Please check the attached exercise on page 3.In this exercise the first step is to check the content of Limestone.data so I entered the following command on R console >limedata=read.table("c:/temp/M5/limestone.dat", header = TRUE) and I got an error message that says "Error in file(file,"rt"): cannot open the connection In addition: Warning message In file(file, "rt") : cannot open file 'c:/temp/M5/limestone.dat': Permission denied Could you please h...

Is there any known incompatibility with the latest 32-bit (i386) CentOS 6 and the latest Lenovo x3650 M5 servers? I?ve been running i386 CentOS 6.X on 3 year old x3650 M4 servers without any issues. Our development environment has not been ported to 64-bit (x86_64) yet, so we are stuck using i386 for another few months. When I try to boot from the netinstall ISO image I just get a ?Boot Failed? mes...

Here's another approach: N <- data.frame(N=c("n1","n2","n3","n4")) M <- data.frame(M=c("m1","m2","m3","m4","m5")) C <- data.frame(n=c("n1","n2","n3"), m=c("m1","m1","m3"), I=c(100,300,400)) # Rebuild the factors using M and N C$m <- factor(as.character(C$m), levels=levels(M$M)) C$n <- factor(as.character(C$n), levels=levels(N$N)) MN...

I am trying to generate a fourth vector,z, given three known and fixed vectors, x1,x2,x3 with corresponding known and fixed correlations with themeselves and with z. That is, all correlations are known and prespecified. How can I do this? Thank you, ben

Hello all, Probably my concepts about the data.frame and matrix and array in R are not clear, I need some clarification to help me understand them better. >M <- read.table("test1.csv",sep=",",row.names=NULL,header=T) gives me: M as M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 1 9 11 14 15 18 20 20 20 20 20 2 3 4 8 9 11 12 14 15 15 15 3 4 5 8 8 9 9 9 9 9 9 4 4 5 7 8 8 8 8 8 8 9 1. How can I read the csv file to: M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 [1,] 9 11 14 15 18 20 20 20 20 20 [2,] 3 4 8 9 11 12 14 15 15 15 [3,] 4...

On 5/13/2016 1:27 PM, Alfred von Campe wrote: > Is there any known incompatibility with the latest 32-bit (i386) CentOS 6 and the latest Lenovo x3650 M5 servers? I?ve been running i386 CentOS 6.X on 3 year old x3650 M4 servers without any issues. Our development environment has not been ported to 64-bit (x86_64) yet, so we are stuck using i386 for another few months. you do know, x86_64 runs i386 apps as long as you install the suitable i386 li...

> On May 13, 2016, at 16:27, I wrote: > > Is there any known incompatibility with the latest 32-bit (i386) CentOS 6 and the latest Lenovo x3650 M5 servers? I?ve been running i386 CentOS 6.X on 3 year old x3650 M4 servers without any issues. Our development environment has not been ported to 64-bit (x86_64) yet, so we are stuck using i386 for another few months. When I try to boot from the netinstall ISO image I just get a ?Boot Failed? mes...

Dear all, please could you advise on the R code I could use in order to do the following operation : a. -- I have 2 lists of "genome coordinates" : a list is composed by numbers that represent genome coordinates; let's say list N : n1 n2 n3 n4 and a list M: m1 m2 m3 m4 m5 2 -- and a data frame C, where for some pairs of coordinates (n,m) from the lists above, we have a numerical intensity; for example : n1; m1; 100 n1; m2; 300 The question would be : what is the most efficient R code I could use in order to integrate the list N, the list M, and the data frame C...