1 Setup

1.1 Packages and options

library(NNbenchmark)
library(kableExtra)
options(scipen = 999)

1.2 Datasets to test

NNdataSummary(NNdatasets)
##      Dataset n_rows n_inputs n_neurons n_parameters
## 1     mDette    500        3         5           26
## 2  mFriedman    500        5         5           36
## 3  mIshigami    500        3        10           51
## 4    mRef153    153        5         3           22
## 5     uDmod1     51        1         6           19
## 6     uDmod2     51        1         5           16
## 7  uDreyfus1     51        1         3           10
## 8  uDreyfus2     51        1         3           10
## 9    uGauss1    250        1         5           16
## 10   uGauss2    250        1         4           13
## 11   uGauss3    250        1         4           13
## 12 uNeuroOne     51        1         2            7

1.3 minpack.lm trainPredict arguments - inputs full fmla, data

if(dir.exists("D:/GSoC2020/Results/2020run03/"))
{  
  odir <- "D:/GSoC2020/Results/2020run03/"
}else if(dir.exists("~/Documents/recherche-enseignement/code/R/NNbenchmark-project/NNtempresult/"))
{  
  odir <- "~/Documents/recherche-enseignement/code/R/NNbenchmark-project/NNtempresult/"
}else
  odir <- "~"

nrep <- 10
maxit2ndorder  <-    200
maxit1storderA <-   1000
maxit1storderB <-  10000
maxit1storderC <- 100000

#library(minpack.lm)
minpack.lm.method <- "none"
hyperParams.minpack.lm <- function(...) {
    return (list(iter=maxit2ndorder))
}
NNtrain.minpack.lm <- function(x, y, dataxy, formula, neur, method, hyperParams, NNfullformula, NNparam, ...) {
    
    hyper_params <- do.call(hyperParams, list(...))

    minpack.lm::nlsLM(NNfullformula, data = dataxy,
                      control = list(maxiter = hyper_params$iter))
}
NNpredict.minpack.lm <- function(object, x, ...)
  predict(object, newdata=as.data.frame(x))
NNclose.minpack.lm <- function()
  if("package:minpack.lm" %in% search())
    detach("package:minpack.lm", unload=TRUE)
minpack.lm.prepareZZ <- list(xdmv = "m", ydmv = "v", zdm = "d", scale = TRUE)
if(FALSE)
res <- trainPredict_1data(1, minpack.lm.method, "NNtrain.minpack.lm", "hyperParams.minpack.lm", "NNpredict.minpack.lm", 
                               NNsummary, "NNclose.minpack.lm", NA, minpack.lm.prepareZZ, nrep=5,
                               echo=TRUE, doplot=FALSE, echoreport=0,
                               pkgname="minpack.lm", pkgfun="nlsLM", csvfile=TRUE, rdafile=TRUE, odir=odir)

2 Launch package’s trainPredict

res <- trainPredict_1pkg(1:12, pkgname = "minpack.lm", pkgfun = "nlsLM", minpack.lm.method,
  prepareZZ.arg = minpack.lm.prepareZZ, nrep = nrep, doplot = TRUE,
  csvfile = TRUE, rdafile = TRUE, odir = odir, echo = FALSE)

3 Results

#print(res)
kable(t(apply(res, c(1,4), min)))%>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"))
RMSE MSE MAE WAE time
mDette 0.6081 0.3698 0.4989 1.9776 0.23
mFriedman 0.1269 0.0161 0.1009 0.3714 0.34
mIshigami 2.5379 6.4408 2.0524 7.6035 0.86
mRef153 3.4318 11.7771 2.4646 13.2825 0.06
uDmod1 0.0445 0.0020 0.0362 0.1153 0.03
uDmod2 0.0427 0.0018 0.0333 0.1058 0.01
uDreyfus1 0.0000 0.0000 0.0000 0.0001 0.00
uDreyfus2 0.0906 0.0082 0.0723 0.2197 0.01
uGauss1 2.2329 4.9857 1.7383 6.9429 0.06
uGauss2 2.9795 8.8775 2.3890 9.0540 0.04
uGauss3 3.1472 9.9050 2.4837 9.7293 0.03
uNeuroOne 1.2720 1.6181 1.1104 2.5150 0.00 
kable(t(apply(res, c(1,4), median)))%>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed"))
RMSE MSE MAE WAE time
mDette 0.6081 0.3698 0.4989 1.9776 0.240
mFriedman 0.1269 0.0161 0.1009 0.3714 0.370
mIshigami 2.5379 6.4408 2.0524 7.6035 0.870
mRef153 3.4318 11.7771 2.4646 13.2825 0.070
uDmod1 0.0445 0.0020 0.0362 0.1153 0.040
uDmod2 0.0427 0.0018 0.0333 0.1058 0.030
uDreyfus1 0.0000 0.0000 0.0000 0.0001 0.015
uDreyfus2 0.0906 0.0082 0.0723 0.2197 0.010
uGauss1 2.2329 4.9857 1.7383 6.9429 0.080
uGauss2 2.9795 8.8775 2.3890 9.0540 0.050
uGauss3 3.1472 9.9050 2.4837 9.7293 0.050
uNeuroOne 1.2720 1.6181 1.1104 2.5150 0.000