search for: binv

...So the processor does in-memory computing, it reads instructions and operands from the memory array, performs the majority operations within the memory array itself. It does instructions using resistive majority which is AB'+B'C+AC Like it does AND operation as 1: 0, 1, @C; //C=0 2: 0, 1, @Binv; //Binv=0 3: 1, @B, @Binv; //Binv=B 4: @A, @Binv, @C; //C=A.B where each operation is a resistive majority and operations are directly performed on the storage of C. It reads @A in a register , @B , reads A and B and directly writes into the memory @C. There are shift operators as well that are a...

...: Franc Brglez Version: R 2.9.1 GUI 1.28 Tiger build 32-bit (5444) OS: MacOSX -- 10.6.2 Submission from: (NULL) (24.148.163.114) I am demonstrating what may be a bug or my lack of experience. Please review as it would help to hear from someone. MANY THANKS -- Franc Brglez The function "binS2binV" returns what I consider a wrong value -- see the terminal output binS2binV = function(string="0001101", sep="") # this procedure is expected to convert a binary string to a binary vector ... # but does it?? Why do we get a vector with quoted binary values?? { qlis...

...tercept (at first) > dim(pred) [1] 940 1 xcol <- 1:dim(pred)[2] > xcol [1] 1 #xcol = 1 , using all number of cols of pred[] > zcol <- c(1) # = 1 , number of cols to use > y.ncol <- dim(y)[2] > n.zcol <- length(zcol) > prior <- list(a=y.ncol, + Binv=diag(y.ncol), + c=n.zcol, + Dinv=diag(n.zcol)) > prior $a [1] 15 $Binv [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [,15] [1,] 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 [2,] 0 1 0...

Hello everyone, I was trying to create an LLVM backend for a processor with a very simple architecture and that does all instructions like load, store, arithmetic and logical instructions using a bunch of majority functions. The processor has only one instruction(majority function) in its ISA and breaks down all other instructions into a number of majority instructions depending on what

...pants dropped out at end. > sim <- read.xport('c:\\xptds.dat') > > int <- rep(1,1200) > y <- cbind(sim$MIY1,sim$TCOV1) > subj <- sim$ID > pred <- cbind(int, sim$TIME, sim$GROUP) > > xcol <- 1:3 > zcol <- 1 > prior <- list(a=2,Binv=4,c=2,Dinv=4) > result <- pan(y,subj,pred,xcol,zcol,prior,seed=13579,iter=1000) Error: subscript out of bounds By the way, I also received the same error message when I tried to include intercept and time in Zcol, a matrix for random effect specification. I used command “ zcol <- 1:2”....

...rior point method; if "fn" then roughly matches with method.cqr="ip" # Create the covariance matrix of X Sigma=matrix(NA,p,p); for(i in 1:p) for(j in 1:p) Sigma[i,j]=0.5^(abs(i-j)) # Generate X (common across all simulations) set.seed(0); X=mvrnorm(n=n,mu=rep(0,p),Sigma=Sigma) Binvlist=list() for(k in 1:K){ tau=cumsum(rep(1/(k+1),k)) Ai=matrix(rep(tau,k),nrow=k,ncol=k,byrow=TRUE) Aj=matrix(rep(tau,k),nrow=k,ncol=k,byrow=FALSE) Amin=pmin(Ai,Aj) # Amin=Ai; Amin[Ai>Aj]=Aj[Ai>Aj] Ax=tau %*% t(tau) B=Amin-Ax Binvlist[[k]]=solve(B) } for(m in 1:M){ mse_wqr_list=mse_c...

...9,9,9, + 10,10,10,10) > pred <- cbind(int=rep(1,40), + dummy1=rep(c(1,0,0,0),10), + dummy2=rep(c(0,1,0,0),10), + dummy3=rep(c(0,0,1,0),10), + dummy4=rep(c(0,2,4,6),10)) > xcol <- 1:4 > zcol <- c(1,5) > a <- array (2,dim=c(2,2)) > prior <- list(a=1,Binv=1,c=2,Dinv=a) > result <- pan(y,subj,pred,xcol,zcol,prior,seed=25679,iter=5000) > result$y [1] 5 8 NaN 10 2 NaN NaN NaN 10 10 8 NaN NaN 10 4 NaN 2 8 NaN [20] 9 NaN NaN NaN NaN 8 6 NaN 5 6 NaN NaN NaN 10 2 NaN NaN 6 8 [39] NaN 2 > [[alternative HT...

...;Boys","Girls")) impht.data$visit <- factor (impht.data$visit) impht.data$code <- factor (impht.data$code) y <- impht.data$htmiss subj <- impht.data$code pred <- cbind (impht.data$age, impht.data$sex, impht.data$visit) xcol <- 1:3 zcol <- 1 prior <- list(a=1, Binv=1, c=1, Dinv=1) ht1 <- pan(y, subj, pred, xcol, zcol, prior, seed=13579, iter=1000) code sex visit age ht htmiss 1 2 1 4.87 105 105 1 2 2 5.86 109.6 1 2 3 6.88 116.4 116.4 1 2 4 7.72 121....

...                                            #col with fixed effect in pred zcol <- 1                                                               #col with random effect in pred  prior <- list( a=ncol(y),                                           #non-informative prior                      Binv= diag( rep(1,ncol(y) ) ) ,                      c= ncol(y) * length(zcol) ,                      Dinv= diag( rep(1 ,ncol(y)*length(zcol) ) )                       )                       imput1 <-  pan(y,subj,pred,xcol,zcol,prior,seed= 13579 ,iter= 1000) #run first imputation ...after 30 mi...