Nothing
EBelasticNet.BinomialCV <- function(BASIS,Target,nFolds,foldId,Epis=FALSE,verbose = 0)
{
nStep = 19;
cat("EBEN Logistic Model,Epis: ",Epis, ";", nFolds, "fold cross-validation\n");
N = nrow(BASIS);
K = ncol(BASIS);
#set.seed(proc.time())
if (missing(foldId))
{
if(N%%nFolds!=0){
foldId = sample(c(rep(1:nFolds,floor(N/nFolds)),1:(N%%nFolds)),N);
}else{
foldId = sample(rep(1:nFolds,floor(N/nFolds)),N);
}
}
lambda_Max = lambdaMax(BASIS,Target,Epis);
lambda_Min = log(0.001*lambda_Max);
step = (log(lambda_Max) - lambda_Min)/nStep;
Lambda = exp(seq(from = log(lambda_Max),to=lambda_Min,by= -step))
N_step = length(Lambda);
step = 1;
Alpha = seq(from = 0.9, to = 0.1, by = -0.1)
nAlpha = length(Alpha);
Likelihood = mat.or.vec((N_step*nAlpha),4);
#logL = mat.or.vec(nFolds,1);
logL1alpha = matrix(0,N_step,2);# temp matrix to keep MSE + std in each step
nLogL = rep(0,4);
pr = "elastic net"; #1LassoNEG; 2: lasso; 3EN
model = "binomial";#0linear; 1 binomial
group = 0;
for(i_alpha in 1:nAlpha){
alpha = Alpha[i_alpha];
if(verbose >=0) cat("Testing alpha", i_alpha, "/",nAlpha,":\t\talpha: ",alpha,"\t")
for (i_s in 1:N_step){
lambda = Lambda[i_s];
hyperpara = c(alpha, lambda);
logL = CVonePair(BASIS,Target,nFolds, foldId,hyperpara,Epis,pr,model,verbose,group);
logL[3] = -logL[3]; #C produces negative logL;
Likelihood[step,] = logL;
logL1alpha[i_s,] = logL[3:4];
step = step + 1;
}
index = which.max(logL1alpha[,1]);
lambda= Lambda[index];
if(verbose >=0) cat("lambda:",lambda,"\t max log Likelihood",logL1alpha[index,1],"\n");
}
colnames(Likelihood) = c("alpha","lambda","logLikelihood","standard error");
index = which.max(Likelihood[,3]);
Res.lambda = Likelihood[index,2];
Res.alpha = Likelihood[index,1];
opt_para = c(Res.alpha,Res.lambda);
result <- list(Likelihood,opt_para);
names(result) <-c("CrossValidation","optimal hyperparameter");
return(result);
}
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