0.3 Main Loop
for (dset in names(NNdatasets)) {
## =============================================
## EXTRACT INFORMATION FROM THE SELECTED DATASET
## =============================================
ds <- NNdatasets[[dset]]$ds
Z <- NNdatasets[[dset]]$Z
neur <- NNdatasets[[dset]]$neur
nparNN <- NNdatasets[[dset]]$nparNN
fmlaNN <- NNdatasets[[dset]]$fmlaNN
donotremove <- c("dset", "dsets", "ds", "Z", "neur", "TF", "nrep", "timer",
"donotremove", "donotremove2")
donotremove2 <- c("dset", "dsets")
## ===================================================
## SELECT THE FORMAT REQUIRED BY THE PACKAGE/ALGORITHM
## d = data.frame, m = matrix, v = vector/numeric
## ATTACH THE OBJECTS CREATED (x, y, Zxy, ... )
## ===================================================
ZZ <- prepareZZ(Z, xdmv = "m", ydmv = "m", scale = T)
attach(ZZ)
## =============================================
## SELECT THE PACKAGE USED FOR TRAINING
## nrep => SELECT THE NUMBER OF INDEPENDANT RUNS
## iter => SELECT THE MAX NUMBER OF ITERATIONS
## TF => PLOT THE RESULTS
## =============================================
nrep <- 10
TF <- TRUE
iter <- 150
descr <- paste(dset, "rminer::fit", sep = "_")
## AUTO
Ypred <- list()
Rmse <- numeric(length = nrep)
Mae <- numeric(length = nrep)
for(i in 1:nrep){
event <- paste0(descr, sprintf("_%.2d", i))
timer$start(event)
#### ADJUST THE FOLLOWING LINES TO THE PACKAGE::ALGORITHM
NNreg <- tryCatch(
rminer::fit(fmla, data = Zxy,
model = "mlp", task = "reg",
size = neur, maxit = iter),
error = function(y) {lm(y ~ 0, data = Zxy)}
)
y_pred <- tryCatch(
ym0 + ysd0*as.numeric(predict(NNreg, newdata = x)),
error = ym0
)
####
Ypred[[i]] <- y_pred
Rmse[i] <- funRMSE(y_pred, y0)
Mae[i] <- funMAE(y_pred, y0)
timer$stop(event, RMSE = Rmse[i], MAE = Mae[i], params = iter, printmsg = FALSE)
}
best <- which(Rmse == min(Rmse, na.rm = TRUE))[1]
best ; Rmse[[best]]
## ================================================
## PLOT ALL MODELS AND THE MODEL WITH THE BEST RMSE
## par OPTIONS CAN BE IMPROVED FOR A BETTER DISPLAY
## ================================================
op <- par(mfcol = c(1,2))
plotNN(xory, y0, uni, TF, main = descr)
for (i in 1:nrep) lipoNN(xory, Ypred[[i]], uni, TF, col = i, lwd = 1)
plotNN(xory, y0, uni, TF, main = descr)
lipoNN(xory, Ypred[[best]], uni, TF, col = 4, lwd = 4)
par(op)
## ===========================
## DETACH ZZ - END OF THE LOOP
## ===========================
detach(ZZ)
}
