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cv.multnet <-function(predmat,y,type.measure,weights,foldid,grouped){
prob_min = 1e-05
prob_max = 1 - prob_min
nc = dim(y)
if (is.null(nc)) {
y = as.factor(y)
ntab = table(y)
nc = as.integer(length(ntab))
y = diag(nc)[as.numeric(y), ,drop=FALSE]
}
else nc = nc[2]
ywt = apply(y, 1, sum)
y = y/ywt
weights = weights * ywt
N = nrow(y) - apply(is.na(predmat[, 1, ,drop=FALSE]), 2, sum)## dimensions could be lost if third dim=1
bigY = array(y, dim(predmat))
predmat=exp(predmat)
predtot=apply(predmat,c(1,3),sum)
for(i in 1:nc)predmat[,i,]=predmat[,i,]/predtot
cvraw = switch(type.measure,
mse = apply((bigY - predmat)^2,c(1, 3), sum),
mae = apply(abs(bigY - predmat), c(1,3), sum),
deviance = { predmat = pmin(pmax(predmat, prob_min), prob_max)
lp = bigY * log(predmat)
ly = bigY * log(bigY)
ly[bigY == 0] = 0
apply(2 * (ly - lp), c(1, 3), sum)
},
class = {
classid = as.numeric(apply(predmat, 3, glmnet_softmax,ignore_labels=TRUE))
yperm = matrix(aperm(bigY, c(1, 3, 2)), ncol = nc)
matrix(1 - yperm[cbind(seq(classid), classid)], ncol = ncol(predtot))
}
)
list(cvraw=cvraw,weights=weights,N=N,type.measure=type.measure,grouped=grouped)
}
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