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predict.dceGMDH <- function(object, x, type = "class", ...){
result <- attr(predict(object$base_models[[1]], x, decision.values = F, probability = TRUE), "probabilities")
ypred_svm <- as.numeric(result[, colnames(result) == "1"])
result <- predict(object$base_models[[2]], x, type = "prob")
ypred_randomForest <- as.numeric(result[, colnames(result) == "1"])
result <- predict(object$base_models[[3]], x, type = "raw")
ypred_naiveBayes <- as.numeric(result[, colnames(result) == "1"])
result <- predict(object$base_models[[4]], x, s = "lambda.min", type = "response")
ypred_cv.glmnet <- result[, 1]
result <- predict(object$base_models[[5]], x, type = "raw")
ypred_nnet <- as.numeric(result[, colnames(result) == "1"])
ypredd <- cbind(ypred_svm,ypred_randomForest,ypred_naiveBayes,ypred_cv.glmnet,ypred_nnet)
store<-NULL
if (object$nlayer==0){
prob <- ypredd[,which.min(object$base_perf)]
}else{
i=1
store[[i]]<-list()
x <- ypredd
idn = c(1:object$sneurons[i])
combinations = t(combn(order(idn), 2))
store[[i]][[1]]<-lapply(1:object$neurons[i+1], function(j) cbind(1, x[, combinations[j, ]]))
store[[i]][[2]]<-lapply(1:object$neurons[i+1], function(j) as.numeric(t(object$architecture[[i]][[2]][[j]]) %*% t(store[[i]][[1]][[j]])))
store[[i]][[3]]<-do.call("cbind", lapply(object$architecture[[i]][[7]], function(j) store[[i]][[2]][[j]]))
if (i != object$nlayer){
repeat{
i<-i+1
store[[i]]<-list()
idn = c(1:object$sneurons[i])
combinations = t(combn(order(idn), 2))
store[[i]][[1]]<-lapply(1:object$neurons[i+1], function(j) cbind(1, store[[i-1]][[3]][, combinations[j, ]]))
store[[i]][[2]]<-lapply(1:object$neurons[i+1], function(j) as.numeric(t(object$architecture[[i]][[2]][[j]]) %*% t(store[[i]][[1]][[j]])))
store[[i]][[3]]<-do.call("cbind", lapply(object$architecture[[i]][[7]], function(j) store[[i]][[2]][[j]]))
if (i == object$nlayer) break
}
}
prob <- as.numeric(store[[object$nlayer]][[2]][[object$architecture[[i]][[8]]]])
}
prob2<-ifelse(prob<0, 0, ifelse(prob>1, 1, prob))
if (type == "probability"){
out<-cbind(1-prob2,prob2)
colnames(out) <- object$levels
}else if (type == "class"){
out <- factor(ifelse(prob2>=0.5,object$levels[2],object$levels[1]))
}else {stop("Please correct type option.")}
return(out)
}
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