Nothing
EBlassoNE.GaussianCV <-
function(BASIS,Target,nFolds,foldId,verbose = 0)
{nStep= 19
#early stop: for each alpha, if next lambda > SSEmin, then stop.
cat("Empirical Bayes LASSO Linear Model (Normal + Exponential prior): ", nFolds, "fold cross-validation\n");
N = nrow(BASIS);
K = ncol(BASIS);
Epis=FALSE;
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(0.0001*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;
nAlpha = 1;
alpha = 1;
MSEcv = mat.or.vec((N_step*nAlpha),4);
MSEeachAlpha = mat.or.vec(nAlpha,4); # minimum MSE for each alpha
MeanSqErr = mat.or.vec(nFolds,1);
SSE1Alpha = matrix(1e10,N_step,2);# temp matrix to keep MSE + std in each step
SSE1Alpha = matrix(1e10,N_step,2);# temp matrix to keep MSE + std in each step
nLogL = rep(0,4);
pr = "lasso"; #1LassoNEG; 2: lasso; 3EN
model = "gaussian";#0linear; 1 binomial
for (i_s in 1:N_step){
lambda = Lambda[i_s];
min_index = which.min(SSE1Alpha[1:(i_s -1),1]);
previousL = SSE1Alpha[min_index,1] + SSE1Alpha[min_index,2];
if(verbose >=0) cat("\tTesting step", step, "\t\tlambda: ",lambda,"\t")
hyperpara = c(alpha, lambda);
logL = CVonePair(BASIS,Target,nFolds, foldId,hyperpara,pr,model,verbose);
SSE1Alpha[i_s,] = logL[3:4];
if(verbose >=0) cat("sum squre error",logL[3],"\n");
MSEcv[step,] = logL;
currentL = MSEcv[step,3];
step = step + 1;
# break out of 2nd for loop
if((currentL - previousL)>0){break;}
}
index = which.min(SSE1Alpha[,1]);
lambda = Lambda[index];
MSEeachAlpha = c(alpha,lambda, SSE1Alpha[index,]);
MSEcv = MSEcv[,1:3];
colnames(MSEcv) = c("lambda","Mean Square Error","standard error");
Res.lambda = MSEeachAlpha[2];
Res.alpha = MSEeachAlpha[1];
result <- list(MSEcv,Res.lambda);
names(result) <-c("CrossValidation","optimal hyperparameter");
return(result);
}
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