search for: peri1

...ts the user to define the data and the parameters and with the rfunc_function he does the minimization.) Mine translation is in R is: where I use a joint function compared to the the above author ================================================================ lw <- function(x, d, im) { peri1 <- per(x) len <- length(x) m <- len/im peri <- peri1[2:(m+1)] z <- c(1:m) freq <- ((2*pi)/len) * z result <- log(sum(freq^(2*d-1)*peri))-(2*d)/m * sum(log(freq)) } ================================================================= which seems to run ok....

...ts the user to define the data and the parameters and with the rfunc_function he does the minimization.) Mine translation is in R is: where I use a joint function compared to the the above author ================================================================ lw <- function(x, d, im) { peri1 <- per(x) len <- length(x) m <- len/im peri <- peri1[2:(m+1)] z <- c(1:m) freq <- ((2*pi)/len) * z result <- log(sum(freq^(2*d-1)*peri))-(2*d)/m * sum(log(freq)) } ================================================================= which seems to run ok....

Hi, I'm currently trying desperately to get the nnet function for training a neural network (with one hidden layer) to perform a regression task. So I run it like the following: trainednet <- nnet(x=traindata, y=trainresponse, size = 30, linout = TRUE, maxit=1000) (where x is a matrix and y a numerical vector consisting of the target values for one variable) To see whether the network

I want to create a neural network, and then everytime it receives new data, instead of creating a new nnet, i want to use a backpropagation algorithm to adjust the weights in the already created nn. I'm using nnet package, I know that nn$wts gives the weights, but I cant find out which weights belong to which conections so I could implement the backpropagation algorithm myself. But if anyone