0.2 Datasets to Test

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 

    method <- c("gaussNewton")
        
    for (m in method) {
        
        descr  <- paste(dset, "brnn::brnn", m, 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(optim_method = m)

            NNreg      <- tryCatch(
                            NNtrain(x = x, y = y, hidden_neur = neur, optim_method = m),
                            error = function(y) {lm(y ~ 0, data = Zxy)}
                          )     
            y_pred     <- tryCatch(
                            ym0 + ysd0 * predict(NNreg, x),
                            error = ym0
                          )     
            ####
            Ypred[[i]] <- y_pred
            Rmse[i]    <- funRMSE(y_pred, y0)
            Mae[i]     <- funMAE(y_pred, y0)
            timer$stop(event, RMSE = Rmse[i], MAE = Mae[i], params = hyper_params$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)
}
## Number of parameters (weights and biases) to estimate: 25 
## Nguyen-Widrow method
## Scaling factor= 0.7022568 
## gamma= 24.6526    alpha= 0.0186   beta= 351.1388 
## Number of parameters (weights and biases) to estimate: 25 
## Nguyen-Widrow method
## Scaling factor= 0.7022568 
## gamma= 23.7098    alpha= 0.0826   beta= 8.8596 
## Number of parameters (weights and biases) to estimate: 25 
## Nguyen-Widrow method
## Scaling factor= 0.7022568 
## gamma= 23.6435    alpha= 0.03     beta= 11.6588 
## Number of parameters (weights and biases) to estimate: 25 
## Nguyen-Widrow method
## Scaling factor= 0.7022568 
## gamma= 24.6593    alpha= 0.0182   beta= 354.662 
## Number of parameters (weights and biases) to estimate: 25 
## Nguyen-Widrow method
## Scaling factor= 0.7022568 
## gamma= 23.7956    alpha= 0.0449   beta= 10.8747 
## Number of parameters (weights and biases) to estimate: 25 
## Nguyen-Widrow method
## Scaling factor= 0.7022568 
## gamma= 19.6799    alpha= 0.1265   beta= 6.9923 
## Number of parameters (weights and biases) to estimate: 25 
## Nguyen-Widrow method
## Scaling factor= 0.7022568 
## gamma= 21.8089    alpha= 0.1219   beta= 7.3302 
## Number of parameters (weights and biases) to estimate: 25 
## Nguyen-Widrow method
## Scaling factor= 0.7022568 
## gamma= 23.7478    alpha= 0.0855   beta= 8.7187 
## Number of parameters (weights and biases) to estimate: 25 
## Nguyen-Widrow method
## Scaling factor= 0.7022568 
## gamma= 23.7142    alpha= 0.0896   beta= 8.6295 
## Number of parameters (weights and biases) to estimate: 25 
## Nguyen-Widrow method
## Scaling factor= 0.7022568 
## gamma= 21.6862    alpha= 0.1111   beta= 7.4059

## Number of parameters (weights and biases) to estimate: 35 
## Nguyen-Widrow method
## Scaling factor= 0.7022568 
## gamma= 34.6333    alpha= 0.1295   beta= 160.6423 
## Number of parameters (weights and biases) to estimate: 35 
## Nguyen-Widrow method
## Scaling factor= 0.7022568 
## gamma= 34.468     alpha= 0.0343   beta= 445.8517 
## Number of parameters (weights and biases) to estimate: 35 
## Nguyen-Widrow method
## Scaling factor= 0.7022568 
## gamma= 34.6337    alpha= 0.1303   beta= 161.0038 
## Number of parameters (weights and biases) to estimate: 35 
## Nguyen-Widrow method
## Scaling factor= 0.7022568 
## gamma= 34.6392    alpha= 0.1244   beta= 167.533 
## Number of parameters (weights and biases) to estimate: 35 
## Nguyen-Widrow method
## Scaling factor= 0.7022568 
## gamma= 34.4348    alpha= 0.0299   beta= 483.9497 
## Number of parameters (weights and biases) to estimate: 35 
## Nguyen-Widrow method
## Scaling factor= 0.7022568 
## gamma= 34.6257    alpha= 0.1289   beta= 162.3004 
## Number of parameters (weights and biases) to estimate: 35 
## Nguyen-Widrow method
## Scaling factor= 0.7022568 
## gamma= 34.4367    alpha= 0.0301   beta= 482.4449 
## Number of parameters (weights and biases) to estimate: 35 
## Nguyen-Widrow method
## Scaling factor= 0.7022568 
## gamma= 34.4586    alpha= 0.0311   beta= 477.0961 
## Number of parameters (weights and biases) to estimate: 35 
## Nguyen-Widrow method
## Scaling factor= 0.7022568 
## gamma= 34.4438    alpha= 0.0074   beta= 657.4524 
## Number of parameters (weights and biases) to estimate: 35 
## Nguyen-Widrow method
## Scaling factor= 0.7022568 
## gamma= 34.6326    alpha= 0.1371   beta= 151.1164

## Number of parameters (weights and biases) to estimate: 50 
## Nguyen-Widrow method
## Scaling factor= 0.7032311 
## gamma= 45.6373    alpha= 0.1098   beta= 13.8367 
## Number of parameters (weights and biases) to estimate: 50 
## Nguyen-Widrow method
## Scaling factor= 0.7032311 
## gamma= 46.1694    alpha= 0.1022   beta= 13.7693 
## Number of parameters (weights and biases) to estimate: 50 
## Nguyen-Widrow method
## Scaling factor= 0.7032311 
## gamma= 24.8253    alpha= 0.3588   beta= 0.9666 
## Number of parameters (weights and biases) to estimate: 50 
## Nguyen-Widrow method
## Scaling factor= 0.7032311 
## gamma= 45.0981    alpha= 0.1017   beta= 13.8904 
## Number of parameters (weights and biases) to estimate: 50 
## Nguyen-Widrow method
## Scaling factor= 0.7032311 
## gamma= 45.9287    alpha= 0.0979   beta= 11.0922 
## Number of parameters (weights and biases) to estimate: 50 
## Nguyen-Widrow method
## Scaling factor= 0.7032311 
## gamma= 23.8185    alpha= 0.3408   beta= 0.959 
## Number of parameters (weights and biases) to estimate: 50 
## Nguyen-Widrow method
## Scaling factor= 0.7032311 
## gamma= 44.4714    alpha= 0.1122   beta= 12.2016 
## Number of parameters (weights and biases) to estimate: 50 
## Nguyen-Widrow method
## Scaling factor= 0.7032311 
## gamma= 44.3894    alpha= 0.0992   beta= 14.1057 
## Number of parameters (weights and biases) to estimate: 50 
## Nguyen-Widrow method
## Scaling factor= 0.7032311 
## gamma= 26.0789    alpha= 0.3749   beta= 0.9599 
## Number of parameters (weights and biases) to estimate: 50 
## Nguyen-Widrow method
## Scaling factor= 0.7032311 
## gamma= 45.4778    alpha= 0.09     beta= 14.1824

## Number of parameters (weights and biases) to estimate: 21 
## Nguyen-Widrow method
## Scaling factor= 0.7050444 
## gamma= 19.1948    alpha= 0.9969   beta= 28.3033 
## Number of parameters (weights and biases) to estimate: 21 
## Nguyen-Widrow method
## Scaling factor= 0.7050444 
## gamma= 18.9029    alpha= 0.9587   beta= 29.2725 
## Number of parameters (weights and biases) to estimate: 21 
## Nguyen-Widrow method
## Scaling factor= 0.7050444 
## gamma= 19.3305    alpha= 0.9155   beta= 30.6316 
## Number of parameters (weights and biases) to estimate: 21 
## Nguyen-Widrow method
## Scaling factor= 0.7050444 
## gamma= 19.3905    alpha= 0.8705   beta= 29.1605 
## Number of parameters (weights and biases) to estimate: 21 
## Nguyen-Widrow method
## Scaling factor= 0.7050444 
## gamma= 19.3295    alpha= 0.9159   beta= 30.6296 
## Number of parameters (weights and biases) to estimate: 21 
## Nguyen-Widrow method
## Scaling factor= 0.7050444 
## gamma= 19.1981    alpha= 0.9955   beta= 28.3088 
## Number of parameters (weights and biases) to estimate: 21 
## Nguyen-Widrow method
## Scaling factor= 0.7050444 
## gamma= 18.7037    alpha= 1.3276   beta= 26.4416 
## Number of parameters (weights and biases) to estimate: 21 
## Nguyen-Widrow method
## Scaling factor= 0.7050444 
## gamma= 18.9033    alpha= 0.9583   beta= 29.2744 
## Number of parameters (weights and biases) to estimate: 21 
## Nguyen-Widrow method
## Scaling factor= 0.7050444 
## gamma= 18.7002    alpha= 1.328    beta= 26.4404 
## Number of parameters (weights and biases) to estimate: 21 
## Nguyen-Widrow method
## Scaling factor= 0.7050444 
## gamma= 19.3286    alpha= 0.9165   beta= 30.6263

## Number of parameters (weights and biases) to estimate: 18 
## Nguyen-Widrow method
## Scaling factor= 4.2 
## gamma= 0      alpha= 64.0625      beta= 0.51 
## Number of parameters (weights and biases) to estimate: 18 
## Nguyen-Widrow method
## Scaling factor= 4.2 
## gamma= 5.6608     alpha= 0.0969   beta= 1.3445 
## Number of parameters (weights and biases) to estimate: 18 
## Nguyen-Widrow method
## Scaling factor= 4.2 
## gamma= 16.5656    alpha= 0.0243   beta= 58.5773 
## Number of parameters (weights and biases) to estimate: 18 
## Nguyen-Widrow method
## Scaling factor= 4.2 
## gamma= 16.1902    alpha= 0.0277   beta= 39.8655 
## Number of parameters (weights and biases) to estimate: 18 
## Nguyen-Widrow method
## Scaling factor= 4.2 
## gamma= 0.4596     alpha= 4.2933   beta= 0.5094 
## Number of parameters (weights and biases) to estimate: 18 
## Nguyen-Widrow method
## Scaling factor= 4.2 
## gamma= 16.5607    alpha= 0.0242   beta= 58.6795 
## Number of parameters (weights and biases) to estimate: 18 
## Nguyen-Widrow method
## Scaling factor= 4.2 
## gamma= 0      alpha= 33.4877      beta= 0.51 
## Number of parameters (weights and biases) to estimate: 18 
## Nguyen-Widrow method
## Scaling factor= 4.2 
## gamma= 16.5654    alpha= 0.0243   beta= 58.5858 
## Number of parameters (weights and biases) to estimate: 18 
## Nguyen-Widrow method
## Scaling factor= 4.2 
## gamma= 16.5612    alpha= 0.0242   beta= 58.6779 
## Number of parameters (weights and biases) to estimate: 18 
## Nguyen-Widrow method
## Scaling factor= 4.2 
## gamma= 16.5655    alpha= 0.0243   beta= 58.5739

## Number of parameters (weights and biases) to estimate: 15 
## Nguyen-Widrow method
## Scaling factor= 3.5 
## gamma= 13.857     alpha= 0.0301   beta= 52.6607 
## Number of parameters (weights and biases) to estimate: 15 
## Nguyen-Widrow method
## Scaling factor= 3.5 
## gamma= 5.5399     alpha= 0.0779   beta= 1.6145 
## Number of parameters (weights and biases) to estimate: 15 
## Nguyen-Widrow method
## Scaling factor= 3.5 
## gamma= 12.5786    alpha= 0.026    beta= 23.2944 
## Number of parameters (weights and biases) to estimate: 15 
## Nguyen-Widrow method
## Scaling factor= 3.5 
## gamma= 5.5256     alpha= 0.0805   beta= 1.602 
## Number of parameters (weights and biases) to estimate: 15 
## Nguyen-Widrow method
## Scaling factor= 3.5 
## gamma= 11.5633    alpha= 0.0346   beta= 16.4319 
## Number of parameters (weights and biases) to estimate: 15 
## Nguyen-Widrow method
## Scaling factor= 3.5 
## gamma= 5.6265     alpha= 0.0792   beta= 1.6126 
## Number of parameters (weights and biases) to estimate: 15 
## Nguyen-Widrow method
## Scaling factor= 3.5 
## gamma= 12.9036    alpha= 0.0271   beta= 22.9985 
## Number of parameters (weights and biases) to estimate: 15 
## Nguyen-Widrow method
## Scaling factor= 3.5 
## gamma= 12.8854    alpha= 0.027    beta= 23.0225 
## Number of parameters (weights and biases) to estimate: 15 
## Nguyen-Widrow method
## Scaling factor= 3.5 
## gamma= 11.1847    alpha= 0.041    beta= 12.3873 
## Number of parameters (weights and biases) to estimate: 15 
## Nguyen-Widrow method
## Scaling factor= 3.5 
## gamma= 11.3702    alpha= 0.036    beta= 15.662

## Number of parameters (weights and biases) to estimate: 9 
## Nguyen-Widrow method
## Scaling factor= 2.1 
## gamma= 8.9926     alpha= 0.0622   beta= 219677.3 
## Number of parameters (weights and biases) to estimate: 9 
## Nguyen-Widrow method
## Scaling factor= 2.1 
## gamma= 8.9857     alpha= 0.0778   beta= 2313.577 
## Number of parameters (weights and biases) to estimate: 9 
## Nguyen-Widrow method
## Scaling factor= 2.1 
## gamma= 8.9879     alpha= 0.0632   beta= 10139.56 
## Number of parameters (weights and biases) to estimate: 9 
## Nguyen-Widrow method
## Scaling factor= 2.1 
## gamma= 8.9756     alpha= 0.0631   beta= 88471.8 
## Number of parameters (weights and biases) to estimate: 9 
## Nguyen-Widrow method
## Scaling factor= 2.1 
## gamma= 8.9484     alpha= 0.0646   beta= 8827.237 
## Number of parameters (weights and biases) to estimate: 9 
## Nguyen-Widrow method
## Scaling factor= 2.1 
## gamma= 8.989      alpha= 0.0625   beta= 65583.79 
## Number of parameters (weights and biases) to estimate: 9 
## Nguyen-Widrow method
## Scaling factor= 2.1 
## gamma= 8.9833     alpha= 0.0627   beta= 78019.69 
## Number of parameters (weights and biases) to estimate: 9 
## Nguyen-Widrow method
## Scaling factor= 2.1 
## gamma= 8.988      alpha= 0.0634   beta= 6627.03 
## Number of parameters (weights and biases) to estimate: 9 
## Nguyen-Widrow method
## Scaling factor= 2.1 
## gamma= 8.987      alpha= 0.0627   beta= 49814.43 
## Number of parameters (weights and biases) to estimate: 9 
## Nguyen-Widrow method
## Scaling factor= 2.1 
## gamma= 8.9755     alpha= 0.0632   beta= 53701.76

## Number of parameters (weights and biases) to estimate: 9 
## Nguyen-Widrow method
## Scaling factor= 2.1 
## gamma= 8.8506     alpha= 0.0675   beta= 130.7886 
## Number of parameters (weights and biases) to estimate: 9 
## Nguyen-Widrow method
## Scaling factor= 2.1 
## gamma= 8.8506     alpha= 0.0675   beta= 130.7886 
## Number of parameters (weights and biases) to estimate: 9 
## Nguyen-Widrow method
## Scaling factor= 2.1 
## gamma= 8.8506     alpha= 0.0675   beta= 130.7886 
## Number of parameters (weights and biases) to estimate: 9 
## Nguyen-Widrow method
## Scaling factor= 2.1 
## gamma= 8.8506     alpha= 0.0675   beta= 130.7886 
## Number of parameters (weights and biases) to estimate: 9 
## Nguyen-Widrow method
## Scaling factor= 2.1 
## gamma= 8.8506     alpha= 0.0675   beta= 130.7886 
## Number of parameters (weights and biases) to estimate: 9 
## Nguyen-Widrow method
## Scaling factor= 2.1 
## gamma= 8.8506     alpha= 0.0675   beta= 130.7886 
## Number of parameters (weights and biases) to estimate: 9 
## Nguyen-Widrow method
## Scaling factor= 2.1 
## gamma= 8.8506     alpha= 0.0675   beta= 130.7886 
## Number of parameters (weights and biases) to estimate: 9 
## Nguyen-Widrow method
## Scaling factor= 2.1 
## gamma= 8.8506     alpha= 0.0675   beta= 130.7886 
## Number of parameters (weights and biases) to estimate: 9 
## Nguyen-Widrow method
## Scaling factor= 2.1 
## gamma= 8.8506     alpha= 0.0675   beta= 130.7886 
## Number of parameters (weights and biases) to estimate: 9 
## Nguyen-Widrow method
## Scaling factor= 2.1 
## gamma= 8.8506     alpha= 0.0675   beta= 130.7886

## Number of parameters (weights and biases) to estimate: 15 
## Nguyen-Widrow method
## Scaling factor= 3.5 
## gamma= 13.0702    alpha= 0.0216   beta= 113.4521 
## Number of parameters (weights and biases) to estimate: 15 
## Nguyen-Widrow method
## Scaling factor= 3.5 
## gamma= 13.4964    alpha= 0.0258   beta= 112.2635 
## Number of parameters (weights and biases) to estimate: 15 
## Nguyen-Widrow method
## Scaling factor= 3.5 
## gamma= 14.7659    alpha= 0.0436   beta= 161.2961 
## Number of parameters (weights and biases) to estimate: 15 
## Nguyen-Widrow method
## Scaling factor= 3.5 
## gamma= 14.7659    alpha= 0.0436   beta= 161.2961 
## Number of parameters (weights and biases) to estimate: 15 
## Nguyen-Widrow method
## Scaling factor= 3.5 
## gamma= 13.666     alpha= 0.0357   beta= 160.259 
## Number of parameters (weights and biases) to estimate: 15 
## Nguyen-Widrow method
## Scaling factor= 3.5 
## gamma= 13.6354    alpha= 0.0263   beta= 121.5528 
## Number of parameters (weights and biases) to estimate: 15 
## Nguyen-Widrow method
## Scaling factor= 3.5 
## gamma= 14.3008    alpha= 0.0311   beta= 162.9315 
## Number of parameters (weights and biases) to estimate: 15 
## Nguyen-Widrow method
## Scaling factor= 3.5 
## gamma= 13.8289    alpha= 0.0114   beta= 17.7978 
## Number of parameters (weights and biases) to estimate: 15 
## Nguyen-Widrow method
## Scaling factor= 3.5 
## gamma= 13.3009    alpha= 0.0183   beta= 141.0345 
## Number of parameters (weights and biases) to estimate: 15 
## Nguyen-Widrow method
## Scaling factor= 3.5 
## gamma= 14.7659    alpha= 0.0436   beta= 161.2961

## Number of parameters (weights and biases) to estimate: 12 
## Nguyen-Widrow method
## Scaling factor= 2.8 
## gamma= 11.5691    alpha= 0.0324   beta= 108.7706 
## Number of parameters (weights and biases) to estimate: 12 
## Nguyen-Widrow method
## Scaling factor= 2.8 
## gamma= 11.6723    alpha= 0.0272   beta= 117.9975 
## Number of parameters (weights and biases) to estimate: 12 
## Nguyen-Widrow method
## Scaling factor= 2.8 
## gamma= 11.6549    alpha= 0.0305   beta= 114.7937 
## Number of parameters (weights and biases) to estimate: 12 
## Nguyen-Widrow method
## Scaling factor= 2.8 
## gamma= 11.6362    alpha= 0.0257   beta= 118.7185 
## Number of parameters (weights and biases) to estimate: 12 
## Nguyen-Widrow method
## Scaling factor= 2.8 
## gamma= 10.4216    alpha= 0.0037   beta= 45.4168 
## Number of parameters (weights and biases) to estimate: 12 
## Nguyen-Widrow method
## Scaling factor= 2.8 
## gamma= 10.4951    alpha= 0.0046   beta= 44.1352 
## Number of parameters (weights and biases) to estimate: 12 
## Nguyen-Widrow method
## Scaling factor= 2.8 
## gamma= 11.6802    alpha= 0.0283   beta= 117.307 
## Number of parameters (weights and biases) to estimate: 12 
## Nguyen-Widrow method
## Scaling factor= 2.8 
## gamma= 11.6639    alpha= 0.0302   beta= 115.3709 
## Number of parameters (weights and biases) to estimate: 12 
## Nguyen-Widrow method
## Scaling factor= 2.8 
## gamma= 11.6046    alpha= 0.0247   beta= 119.133 
## Number of parameters (weights and biases) to estimate: 12 
## Nguyen-Widrow method
## Scaling factor= 2.8 
## gamma= 11.3222    alpha= 0.0339   beta= 101.7393

## Number of parameters (weights and biases) to estimate: 12 
## Nguyen-Widrow method
## Scaling factor= 2.8 
## gamma= 11.8754    alpha= 0.0481   beta= 96.029 
## Number of parameters (weights and biases) to estimate: 12 
## Nguyen-Widrow method
## Scaling factor= 2.8 
## gamma= 11.7208    alpha= 0.0458   beta= 101.8855 
## Number of parameters (weights and biases) to estimate: 12 
## Nguyen-Widrow method
## Scaling factor= 2.8 
## gamma= 11.6896    alpha= 0.0483   beta= 75.1771 
## Number of parameters (weights and biases) to estimate: 12 
## Nguyen-Widrow method
## Scaling factor= 2.8 
## gamma= 11.8754    alpha= 0.0481   beta= 96.0291 
## Number of parameters (weights and biases) to estimate: 12 
## Nguyen-Widrow method
## Scaling factor= 2.8 
## gamma= 11.1364    alpha= 0.0546   beta= 123.8739 
## Number of parameters (weights and biases) to estimate: 12 
## Nguyen-Widrow method
## Scaling factor= 2.8 
## gamma= 11.8754    alpha= 0.0481   beta= 96.029 
## Number of parameters (weights and biases) to estimate: 12 
## Nguyen-Widrow method
## Scaling factor= 2.8 
## gamma= 11.7763    alpha= 0.0367   beta= 124.1513 
## Number of parameters (weights and biases) to estimate: 12 
## Nguyen-Widrow method
## Scaling factor= 2.8 
## gamma= 11.6896    alpha= 0.0483   beta= 75.1766 
## Number of parameters (weights and biases) to estimate: 12 
## Nguyen-Widrow method
## Scaling factor= 2.8 
## gamma= 11.6897    alpha= 0.0483   beta= 75.1764 
## Number of parameters (weights and biases) to estimate: 12 
## Nguyen-Widrow method
## Scaling factor= 2.8 
## gamma= 11.7763    alpha= 0.0367   beta= 124.1513

## Number of parameters (weights and biases) to estimate: 6 
## Nguyen-Widrow method
## Scaling factor= 1.4 
## gamma= 5.7327     alpha= 0.0814   beta= 5.9026 
## Number of parameters (weights and biases) to estimate: 6 
## Nguyen-Widrow method
## Scaling factor= 1.4 
## gamma= 5.7328     alpha= 0.0813   beta= 5.903 
## Number of parameters (weights and biases) to estimate: 6 
## Nguyen-Widrow method
## Scaling factor= 1.4 
## gamma= 5.7327     alpha= 0.0814   beta= 5.9026 
## Number of parameters (weights and biases) to estimate: 6 
## Nguyen-Widrow method
## Scaling factor= 1.4 
## gamma= 5.7327     alpha= 0.0814   beta= 5.9027 
## Number of parameters (weights and biases) to estimate: 6 
## Nguyen-Widrow method
## Scaling factor= 1.4 
## gamma= 5.7328     alpha= 0.0814   beta= 5.9028 
## Number of parameters (weights and biases) to estimate: 6 
## Nguyen-Widrow method
## Scaling factor= 1.4 
## gamma= 5.7327     alpha= 0.0813   beta= 5.9031 
## Number of parameters (weights and biases) to estimate: 6 
## Nguyen-Widrow method
## Scaling factor= 1.4 
## gamma= 5.7328     alpha= 0.0814   beta= 5.9025 
## Number of parameters (weights and biases) to estimate: 6 
## Nguyen-Widrow method
## Scaling factor= 1.4 
## gamma= 5.7326     alpha= 0.0813   beta= 5.903 
## Number of parameters (weights and biases) to estimate: 6 
## Nguyen-Widrow method
## Scaling factor= 1.4 
## gamma= 5.7327     alpha= 0.0814   beta= 5.9025 
## Number of parameters (weights and biases) to estimate: 6 
## Nguyen-Widrow method
## Scaling factor= 1.4 
## gamma= 5.7327     alpha= 0.0813   beta= 5.903

0.5 Results

0.6 Best Results

dataset method minRMSE meanRMSE meanTime
mDette gaussNewton 0.2992 1.59958 0.090
mFriedman 0.0063 0.00991 0.109
mIshigami 0.6612 1.25819 0.183
mRef153 3.3425 3.44719 0.021
uDmod1 0.0451 0.23851 0.025
uDmod2 0.0435 0.13234 0.025
uDreyfus1 0.0022 0.00787 0.008
uDreyfus2 0.0913 0.09130 0.009
uGauss1 2.2435 2.84815 0.034
uGauss2 2.3889 2.73494 0.040
uGauss3 2.4870 2.82846 0.023
uNeuroOne 0.3523 0.35230 0.008