Nothing
elmtrain.default <-
function(x,y,nhid,actfun,...) {
require(MASS)
if(nhid < 1) stop("ERROR: number of hidden neurons must be >= 1")
T <- t(y)
P <- t(x)
inpweight <- randomMatrix(nrow(P),nhid,-1,1)
tempH <- inpweight %*% P
biashid <- runif(nhid,min=-1,max=1)
biasMatrix <- matrix(rep(biashid, ncol(P)), nrow=nhid, ncol=ncol(P), byrow = F)
tempH = tempH + biasMatrix
if(actfun == "sig") H = 1 / (1 + exp(-1*tempH))
else {
if(actfun == "sin") H = sin(tempH)
else {
if(actfun == "radbas") H = exp(-1*(tempH^2))
else {
if(actfun == "hardlim") H = hardlim(tempH)
else {
if(actfun == "hardlims") H = hardlims(tempH)
else {
if(actfun == "satlins") H = satlins(tempH)
else {
if(actfun == "tansig") H = 2/(1+exp(-2*tempH))-1
else {
if(actfun == "tribas") H = tribas(tempH)
else {
if(actfun == "poslin") H = poslin(tempH)
else {
if(actfun == "purelin") H = tempH
else stop(paste("ERROR: ",actfun," is not a valid activation function.",sep=""))
}
}
}
}
}
}
}
}
}
outweight <- ginv(t(H), tol = sqrt(.Machine$double.eps)) %*% t(T)
Y <- t(t(H) %*% outweight)
model = list(inpweight=inpweight,biashid=biashid,outweight=outweight,actfun=actfun,nhid=nhid,predictions=t(Y))
model$fitted.values <- t(Y)
model$residuals <- y - model$fitted.values
model$call <- match.call()
class(model) <- "elmNN"
model
}
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