Displaying 4 results from an estimated 4 matches for "nifold".
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2004 Mar 30
1
classification with nnet: handling unequal class sizes
...(...))
diag(tab) <- 0
cat("error rate = ",
round(100*sum(tab)/length(list(...)[[1]]), 2), "%\n")
invisible()
}
CVnn2 <- function(formula, data,
size = c(0,4,4,10,10), lambda = c(0, rep(c(0.001,
0.01),2)),
nreps = 1, nifold = 5, verbose = 99, ...)
{
resmatrix <- function(predict.matrix,learn, data, ri, i)
{
rae.matrix <- predict.matrix
rae.matrix[,] <- 0
rae.vector <- as.numeric(as.factor((predict(learn, data[ri ==
i,], type = "class"))))
for (k in 1:dim(rae....
2004 Sep 23
0
nnet and weights: error analysis using V&R example
...and <- sample(10, dim(fgl)[1], replace = T)
fgl1 <- fgl
fgl1[1:9] <- lapply(fgl[, 1:9], function(x) {r <- range(x); (x -
r[1])/diff(r)})
CVnn2 <- function(formula, data,
size = c(0,4,4,10,10), lambda = c(0, rep(c(0.001,
0.01),2)),
nreps = 1, nifold = 5, verbose = 99, ...)
{
CVnn1 <- function(formula, data, nreps=1, ri, verbose, ...)
{
totalerror <- 0
truth <- data[,deparse(formula[[2]])]
res <- matrix(0, nrow(data), length(levels(truth)))
if(verbose > 20) cat(" inner fold&q...
2004 Sep 23
0
nnet with weights parameter: odd error
...problem- it is really very
strange and I need your help! I tried it very simple in defining the
weights as = 1 for each obs (as it is by default)!:
CVnn2 <- function(formula, data,
size = c(0,4,4,10,10), lambda = c(0, rep(c(0.001,
0.01),2)),
nreps = 1, nifold = 5, verbose = 99, ...)
{
resmatrix <- function(predict.matrix, learn, data, ri, i)
{
rae.matrix <- predict.matrix
rae.matrix[,] <- 0
rae.vector <- as.numeric(as.factor((predict(learn, data[ri == i,],...
2011 Jan 05
0
Nnet and AIC: selection of a parsimonious parameterisation
...excludes names, authors? predictions
attach(cpus2)
cpus3 <- data.frame(syct = syct-2, mmin = mmin-3, mmax = mmax-4,
cach=cach/256,chmin=chmin/100, chmax=chmax/100, perf)
detach()
CVnn.cpus <- function(formula, data = cpus3[cpus.samp, ], maxSize = 10,
decayRange = c(0,0.2), nreps = 5, nifold = 10, alpha= 9/10,
linout = TRUE, skip = TRUE, maxit = 1000,...){
#nreps=number of attempts to fit a nnet model with randomly chosen initial
parameters
# The one with the smallest RSS on the training data is then chosen
nnWtsPrunning <-function(nn,data,alpha,i){
truth <- log10...