Nothing
compute <-
function (x, covariate, rep = 1)
{
nn <- x
linear.output <- nn$linear.output
weights <- nn$weights[[rep]]
nrow.weights <- sapply(weights, nrow)
ncol.weights <- sapply(weights, ncol)
weights <- unlist(weights)
if (any(is.na(weights)))
weights[is.na(weights)] <- 0
weights <- relist(weights, nrow.weights, ncol.weights)
length.weights <- length(weights)
# covariate <- as.matrix(cbind(1, covariate))
if (testClass(covariate,'dgCMatrix')) {
covariate <- cbind(1, covariate)
} else {
covariate <- cbind(1, covariate)
}
act.fct <- nn$act.fct
neurons <- list(covariate)
if (length.weights > 1)
for (i in 1:(length.weights - 1)) {
temp <- neurons[[i]] %*% weights[[i]]
act.temp <- act.fct(temp)
neurons[[i + 1]] <- cbind(1, act.temp)
}
temp <- neurons[[length.weights]] %*% weights[[length.weights]]
if (linear.output)
net.result <- temp
else net.result <- act.fct(temp)
list(neurons = neurons, net.result = net.result)
}
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