NNbenchmark

library(NNbenchmark)
library(nlsr)

library(stringr)
library(dplyr)
library(kableExtra)

0.1 Set Environment

options(scipen = 9999)
options("digits.secs" = 2)
timer  <- createTimer(verbose = FALSE)

0.2 Datasets to Test

NNdataSummary(NNdatasets)

0.3 NN Train Function

hyperParams <- function(nparNN) {
    
    iter       <- 150
    
    bb         <- round(rnorm(nparNN, sd = 0.1), 4)
    names(bb)  <- paste0("b", 1:nparNN)
    
    params     <- paste0("method=","nlxb", "_iter=", iter)
    
    out        <- list(bb = bb, iter = iter, params = params)
    
    return(out)
}



NNtrain <- function(fmlaNN, Zxy, nparNN) {
    
    hyper_params <- hyperParams(nparNN)
    bb <- hyper_params$bb
    iter <- hyper_params$iter
    
    NNreg <- nlsr::nlxb(formula = fmlaNN, 
                        start = bb, 
                        data = Zxy,
                        control = list(femax = iter))
    return(NNreg)
}

0.4 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 = "v", zdm = "d", scale = TRUE)
    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 

    descr  <- paste(dset, "nlsr::nlxb", 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
            
        hyper_params <- hyperParams(nparNN)

        NNreg      <- tryCatch(
                        NNtrain(fmlaNN = fmlaNN, Zxy = Zxy, nparNN = nparNN),
                        error = function(y) {lm(y ~ 0, data = Zxy)}
                        )
        y_pred     <- tryCatch(
                        ym0 + ysd0*as.numeric(predict(NNreg, Zxy)),
                        error = function(NNreg) rep(ym0, nrow(Zxy))
                        )    
        ####
        Ypred[[i]] <- y_pred
        Rmse[i]    <- funRMSE(y_pred, y0)
        Mae[i]     <- funMAE(y_pred, y0)
        timer$stop(event, RMSE = Rmse[i], MAE = Mae[i], params = hyper_params$params, 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)
}

## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x1"  "b5"  "x2"  "b6"  "x3"  "b7"  "b8" 
## [13] "b9"  "b10" "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18" "b19" "b20"
## [25] "b21" "b22" "b23" "b24" "b25" "b26"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x1"  "b5"  "x2"  "b6"  "x3"  "b7"  "b8" 
## [13] "b9"  "b10" "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18" "b19" "b20"
## [25] "b21" "b22" "b23" "b24" "b25" "b26"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x1"  "b5"  "x2"  "b6"  "x3"  "b7"  "b8" 
## [13] "b9"  "b10" "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18" "b19" "b20"
## [25] "b21" "b22" "b23" "b24" "b25" "b26"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x1"  "b5"  "x2"  "b6"  "x3"  "b7"  "b8" 
## [13] "b9"  "b10" "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18" "b19" "b20"
## [25] "b21" "b22" "b23" "b24" "b25" "b26"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x1"  "b5"  "x2"  "b6"  "x3"  "b7"  "b8" 
## [13] "b9"  "b10" "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18" "b19" "b20"
## [25] "b21" "b22" "b23" "b24" "b25" "b26"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x1"  "b5"  "x2"  "b6"  "x3"  "b7"  "b8" 
## [13] "b9"  "b10" "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18" "b19" "b20"
## [25] "b21" "b22" "b23" "b24" "b25" "b26"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x1"  "b5"  "x2"  "b6"  "x3"  "b7"  "b8" 
## [13] "b9"  "b10" "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18" "b19" "b20"
## [25] "b21" "b22" "b23" "b24" "b25" "b26"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x1"  "b5"  "x2"  "b6"  "x3"  "b7"  "b8" 
## [13] "b9"  "b10" "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18" "b19" "b20"
## [25] "b21" "b22" "b23" "b24" "b25" "b26"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x1"  "b5"  "x2"  "b6"  "x3"  "b7"  "b8" 
## [13] "b9"  "b10" "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18" "b19" "b20"
## [25] "b21" "b22" "b23" "b24" "b25" "b26"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x1"  "b5"  "x2"  "b6"  "x3"  "b7"  "b8" 
## [13] "b9"  "b10" "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18" "b19" "b20"
## [25] "b21" "b22" "b23" "b24" "b25" "b26"
## no weights

## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x1"  "b5"  "x2"  "b6"  "x3"  "b7"  "x4" 
## [13] "b8"  "x5"  "b9"  "b10" "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18"
## [25] "b19" "b20" "b21" "b22" "b23" "b24" "b25" "b26" "b27" "b28" "b29" "b30"
## [37] "b31" "b32" "b33" "b34" "b35" "b36"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x1"  "b5"  "x2"  "b6"  "x3"  "b7"  "x4" 
## [13] "b8"  "x5"  "b9"  "b10" "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18"
## [25] "b19" "b20" "b21" "b22" "b23" "b24" "b25" "b26" "b27" "b28" "b29" "b30"
## [37] "b31" "b32" "b33" "b34" "b35" "b36"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x1"  "b5"  "x2"  "b6"  "x3"  "b7"  "x4" 
## [13] "b8"  "x5"  "b9"  "b10" "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18"
## [25] "b19" "b20" "b21" "b22" "b23" "b24" "b25" "b26" "b27" "b28" "b29" "b30"
## [37] "b31" "b32" "b33" "b34" "b35" "b36"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x1"  "b5"  "x2"  "b6"  "x3"  "b7"  "x4" 
## [13] "b8"  "x5"  "b9"  "b10" "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18"
## [25] "b19" "b20" "b21" "b22" "b23" "b24" "b25" "b26" "b27" "b28" "b29" "b30"
## [37] "b31" "b32" "b33" "b34" "b35" "b36"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x1"  "b5"  "x2"  "b6"  "x3"  "b7"  "x4" 
## [13] "b8"  "x5"  "b9"  "b10" "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18"
## [25] "b19" "b20" "b21" "b22" "b23" "b24" "b25" "b26" "b27" "b28" "b29" "b30"
## [37] "b31" "b32" "b33" "b34" "b35" "b36"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x1"  "b5"  "x2"  "b6"  "x3"  "b7"  "x4" 
## [13] "b8"  "x5"  "b9"  "b10" "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18"
## [25] "b19" "b20" "b21" "b22" "b23" "b24" "b25" "b26" "b27" "b28" "b29" "b30"
## [37] "b31" "b32" "b33" "b34" "b35" "b36"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x1"  "b5"  "x2"  "b6"  "x3"  "b7"  "x4" 
## [13] "b8"  "x5"  "b9"  "b10" "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18"
## [25] "b19" "b20" "b21" "b22" "b23" "b24" "b25" "b26" "b27" "b28" "b29" "b30"
## [37] "b31" "b32" "b33" "b34" "b35" "b36"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x1"  "b5"  "x2"  "b6"  "x3"  "b7"  "x4" 
## [13] "b8"  "x5"  "b9"  "b10" "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18"
## [25] "b19" "b20" "b21" "b22" "b23" "b24" "b25" "b26" "b27" "b28" "b29" "b30"
## [37] "b31" "b32" "b33" "b34" "b35" "b36"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x1"  "b5"  "x2"  "b6"  "x3"  "b7"  "x4" 
## [13] "b8"  "x5"  "b9"  "b10" "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18"
## [25] "b19" "b20" "b21" "b22" "b23" "b24" "b25" "b26" "b27" "b28" "b29" "b30"
## [37] "b31" "b32" "b33" "b34" "b35" "b36"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x1"  "b5"  "x2"  "b6"  "x3"  "b7"  "x4" 
## [13] "b8"  "x5"  "b9"  "b10" "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18"
## [25] "b19" "b20" "b21" "b22" "b23" "b24" "b25" "b26" "b27" "b28" "b29" "b30"
## [37] "b31" "b32" "b33" "b34" "b35" "b36"
## no weights

## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x1"  "b5"  "x2"  "b6"  "x3"  "b7"  "b8" 
## [13] "b9"  "b10" "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18" "b19" "b20"
## [25] "b21" "b22" "b23" "b24" "b25" "b26" "b27" "b28" "b29" "b30" "b31" "b32"
## [37] "b33" "b34" "b35" "b36" "b37" "b38" "b39" "b40" "b41" "b42" "b43" "b44"
## [49] "b45" "b46" "b47" "b48" "b49" "b50" "b51"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x1"  "b5"  "x2"  "b6"  "x3"  "b7"  "b8" 
## [13] "b9"  "b10" "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18" "b19" "b20"
## [25] "b21" "b22" "b23" "b24" "b25" "b26" "b27" "b28" "b29" "b30" "b31" "b32"
## [37] "b33" "b34" "b35" "b36" "b37" "b38" "b39" "b40" "b41" "b42" "b43" "b44"
## [49] "b45" "b46" "b47" "b48" "b49" "b50" "b51"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x1"  "b5"  "x2"  "b6"  "x3"  "b7"  "b8" 
## [13] "b9"  "b10" "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18" "b19" "b20"
## [25] "b21" "b22" "b23" "b24" "b25" "b26" "b27" "b28" "b29" "b30" "b31" "b32"
## [37] "b33" "b34" "b35" "b36" "b37" "b38" "b39" "b40" "b41" "b42" "b43" "b44"
## [49] "b45" "b46" "b47" "b48" "b49" "b50" "b51"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x1"  "b5"  "x2"  "b6"  "x3"  "b7"  "b8" 
## [13] "b9"  "b10" "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18" "b19" "b20"
## [25] "b21" "b22" "b23" "b24" "b25" "b26" "b27" "b28" "b29" "b30" "b31" "b32"
## [37] "b33" "b34" "b35" "b36" "b37" "b38" "b39" "b40" "b41" "b42" "b43" "b44"
## [49] "b45" "b46" "b47" "b48" "b49" "b50" "b51"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x1"  "b5"  "x2"  "b6"  "x3"  "b7"  "b8" 
## [13] "b9"  "b10" "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18" "b19" "b20"
## [25] "b21" "b22" "b23" "b24" "b25" "b26" "b27" "b28" "b29" "b30" "b31" "b32"
## [37] "b33" "b34" "b35" "b36" "b37" "b38" "b39" "b40" "b41" "b42" "b43" "b44"
## [49] "b45" "b46" "b47" "b48" "b49" "b50" "b51"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x1"  "b5"  "x2"  "b6"  "x3"  "b7"  "b8" 
## [13] "b9"  "b10" "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18" "b19" "b20"
## [25] "b21" "b22" "b23" "b24" "b25" "b26" "b27" "b28" "b29" "b30" "b31" "b32"
## [37] "b33" "b34" "b35" "b36" "b37" "b38" "b39" "b40" "b41" "b42" "b43" "b44"
## [49] "b45" "b46" "b47" "b48" "b49" "b50" "b51"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x1"  "b5"  "x2"  "b6"  "x3"  "b7"  "b8" 
## [13] "b9"  "b10" "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18" "b19" "b20"
## [25] "b21" "b22" "b23" "b24" "b25" "b26" "b27" "b28" "b29" "b30" "b31" "b32"
## [37] "b33" "b34" "b35" "b36" "b37" "b38" "b39" "b40" "b41" "b42" "b43" "b44"
## [49] "b45" "b46" "b47" "b48" "b49" "b50" "b51"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x1"  "b5"  "x2"  "b6"  "x3"  "b7"  "b8" 
## [13] "b9"  "b10" "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18" "b19" "b20"
## [25] "b21" "b22" "b23" "b24" "b25" "b26" "b27" "b28" "b29" "b30" "b31" "b32"
## [37] "b33" "b34" "b35" "b36" "b37" "b38" "b39" "b40" "b41" "b42" "b43" "b44"
## [49] "b45" "b46" "b47" "b48" "b49" "b50" "b51"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x1"  "b5"  "x2"  "b6"  "x3"  "b7"  "b8" 
## [13] "b9"  "b10" "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18" "b19" "b20"
## [25] "b21" "b22" "b23" "b24" "b25" "b26" "b27" "b28" "b29" "b30" "b31" "b32"
## [37] "b33" "b34" "b35" "b36" "b37" "b38" "b39" "b40" "b41" "b42" "b43" "b44"
## [49] "b45" "b46" "b47" "b48" "b49" "b50" "b51"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x1"  "b5"  "x2"  "b6"  "x3"  "b7"  "b8" 
## [13] "b9"  "b10" "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18" "b19" "b20"
## [25] "b21" "b22" "b23" "b24" "b25" "b26" "b27" "b28" "b29" "b30" "b31" "b32"
## [37] "b33" "b34" "b35" "b36" "b37" "b38" "b39" "b40" "b41" "b42" "b43" "b44"
## [49] "b45" "b46" "b47" "b48" "b49" "b50" "b51"
## no weights

## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x1"  "b5"  "x2"  "b6"  "x3"  "b7"  "x4" 
## [13] "b8"  "x5"  "b9"  "b10" "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18"
## [25] "b19" "b20" "b21" "b22"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x1"  "b5"  "x2"  "b6"  "x3"  "b7"  "x4" 
## [13] "b8"  "x5"  "b9"  "b10" "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18"
## [25] "b19" "b20" "b21" "b22"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x1"  "b5"  "x2"  "b6"  "x3"  "b7"  "x4" 
## [13] "b8"  "x5"  "b9"  "b10" "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18"
## [25] "b19" "b20" "b21" "b22"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x1"  "b5"  "x2"  "b6"  "x3"  "b7"  "x4" 
## [13] "b8"  "x5"  "b9"  "b10" "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18"
## [25] "b19" "b20" "b21" "b22"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x1"  "b5"  "x2"  "b6"  "x3"  "b7"  "x4" 
## [13] "b8"  "x5"  "b9"  "b10" "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18"
## [25] "b19" "b20" "b21" "b22"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x1"  "b5"  "x2"  "b6"  "x3"  "b7"  "x4" 
## [13] "b8"  "x5"  "b9"  "b10" "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18"
## [25] "b19" "b20" "b21" "b22"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x1"  "b5"  "x2"  "b6"  "x3"  "b7"  "x4" 
## [13] "b8"  "x5"  "b9"  "b10" "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18"
## [25] "b19" "b20" "b21" "b22"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x1"  "b5"  "x2"  "b6"  "x3"  "b7"  "x4" 
## [13] "b8"  "x5"  "b9"  "b10" "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18"
## [25] "b19" "b20" "b21" "b22"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x1"  "b5"  "x2"  "b6"  "x3"  "b7"  "x4" 
## [13] "b8"  "x5"  "b9"  "b10" "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18"
## [25] "b19" "b20" "b21" "b22"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x1"  "b5"  "x2"  "b6"  "x3"  "b7"  "x4" 
## [13] "b8"  "x5"  "b9"  "b10" "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18"
## [25] "b19" "b20" "b21" "b22"
## no weights

## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18" "b19"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18" "b19"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18" "b19"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18" "b19"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18" "b19"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18" "b19"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18" "b19"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18" "b19"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18" "b19"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13" "b14" "b15" "b16" "b17" "b18" "b19"
## no weights

## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13" "b14" "b15" "b16"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13" "b14" "b15" "b16"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13" "b14" "b15" "b16"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13" "b14" "b15" "b16"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13" "b14" "b15" "b16"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13" "b14" "b15" "b16"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13" "b14" "b15" "b16"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13" "b14" "b15" "b16"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13" "b14" "b15" "b16"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13" "b14" "b15" "b16"
## no weights

## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## no weights

## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## no weights

## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13" "b14" "b15" "b16"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13" "b14" "b15" "b16"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13" "b14" "b15" "b16"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13" "b14" "b15" "b16"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13" "b14" "b15" "b16"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13" "b14" "b15" "b16"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13" "b14" "b15" "b16"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13" "b14" "b15" "b16"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13" "b14" "b15" "b16"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13" "b14" "b15" "b16"
## no weights

## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13"
## no weights

## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13"
## no weights
## vn: [1] "y"   "b1"  "b2"  "b3"  "b4"  "x"   "b5"  "b6"  "b7"  "b8"  "b9"  "b10"
## [13] "b11" "b12" "b13"
## no weights

## vn:[1] "y"  "b1" "b2" "b3" "b4" "x"  "b5" "b6" "b7"
## no weights
## vn:[1] "y"  "b1" "b2" "b3" "b4" "x"  "b5" "b6" "b7"
## no weights
## vn:[1] "y"  "b1" "b2" "b3" "b4" "x"  "b5" "b6" "b7"
## no weights
## vn:[1] "y"  "b1" "b2" "b3" "b4" "x"  "b5" "b6" "b7"
## no weights
## vn:[1] "y"  "b1" "b2" "b3" "b4" "x"  "b5" "b6" "b7"
## no weights
## vn:[1] "y"  "b1" "b2" "b3" "b4" "x"  "b5" "b6" "b7"
## no weights
## vn:[1] "y"  "b1" "b2" "b3" "b4" "x"  "b5" "b6" "b7"
## no weights
## vn:[1] "y"  "b1" "b2" "b3" "b4" "x"  "b5" "b6" "b7"
## no weights
## vn:[1] "y"  "b1" "b2" "b3" "b4" "x"  "b5" "b6" "b7"
## no weights
## vn:[1] "y"  "b1" "b2" "b3" "b4" "x"  "b5" "b6" "b7"
## no weights

0.5 Results

dfr0 <- getTimer(timer) 

dfr  <- data.frame(
    ds_pkg.fun_algo = stringr::str_sub(dfr0[ ,1], 1, -4),
    run     = stringr::str_sub(dfr0[ ,1], -2, -1),
    dfr0[, c("RMSE","MAE")],
    dataset = stringr::str_replace_all(stringr::str_extract(dfr0[, 1], pattern = "^\\w*_"), fixed("_"), ""),
    method = stringr::str_replace_all(stringr::str_extract(dfr0[, 1], pattern = "_\\w*_"), fixed("_"), ""),
    Elapsed = round(dfr0[ ,4], 5),
    params = dfr0$params
)


dfr %>%
    select(-c(dataset, method))

0.6 Best Results

dfr %>%
    group_by(dataset, method) %>%
    summarise(minRMSE = min(RMSE), meanRMSE = mean(RMSE), meanTime = mean(Elapsed)) %>%
    kable() %>%
    kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>%
    collapse_rows(columns = 1:2, valign = "top")

dataset	method	minRMSE	meanRMSE	meanTime
mDette	NA	0.2015	0.39495	0.811
mFriedman	NA	0.0047	0.02499	0.773
mIshigami	NA	0.6399	2.07440	2.519
mRef153	NA	3.1128	3.58859	0.285
uDmod1	NA	0.0433	0.04787	0.229
uDmod2	NA	0.0405	0.06418	0.183
uDreyfus1	NA	0.0000	0.02226	0.060
uDreyfus2	NA	0.0906	0.10066	0.146
uGauss1	NA	2.2313	5.28806	0.317
uGauss2	NA	2.3344	4.71213	0.226
uGauss3	NA	2.2995	2.77101	0.269
uNeuroOne	NA	0.2830	0.31399	0.032

clearNN(donotremove)

NNbenchmark | nlsr

Salsabila Mahdi

0.1 Set Environment

0.2 Datasets to Test

0.3 NN Train Function

0.4 Main Loop

0.5 Results

0.6 Best Results