Setup
Packages and options
library(NNbenchmark)
library(kableExtra)
options(scipen = 999)
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
brnn trainPredict arguments - inputs x, y
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(brnn)
brnn.method <- "Gauss-Newton"
hyperParams.brnn <- function(optim_method, ...) {
return(list(iter = maxit2ndorder))
}
NNtrain.brnn <- function(x, y, dataxy, formula, neur, optim_method, hyperParams,...) {
hyper_params <- do.call(hyperParams.brnn, list(brnn.method))
iter <- hyper_params$iter
NNreg <- brnn::brnn(x, y, neur, normalize = FALSE, epochs = iter, verbose = FALSE)
return (NNreg)
}
NNpredict.brnn <- function(object, x, ...)
predict(object, x)
NNclose.brnn <- function()
if("package:brnn" %in% search())
detach("package:brnn", unload=TRUE)
brnn.prepareZZ <- list(xdmv = "m", ydmv = "v", zdm = "d", scale = TRUE)
if(FALSE)
res <- trainPredict_1data(1, brnn.method, "NNtrain.brnn", "hyperParams.brnn", "NNpredict.brnn",
NNsummary, "NNclose.brnn", NA, brnn.prepareZZ, nrep=5, echo=TRUE, doplot=FALSE,
pkgname="brnn", pkgfun="brnn", csvfile=TRUE, rdafile=TRUE, odir=odir)
if(FALSE)
res <- trainPredict_1mth1data(1, brnn.method[1], "NNtrain.brnn", "hyperParams.brnn", "NNpredict.brnn",
NNsummary, brnn.prepareZZ, nrep=5, echo=TRUE, doplot=FALSE,
pkgname="brnn", pkgfun="brnn", csvfile=TRUE, rdafile=TRUE, odir=odir)
Launch package’s trainPredict
res <- trainPredict_1pkg(1:12, pkgname = "brnn", pkgfun = "brnn", brnn.method,
prepareZZ.arg = brnn.prepareZZ, nrep = nrep, doplot = TRUE,
csvfile = TRUE, rdafile = TRUE, odir = odir, echo = FALSE)
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
|
0.2295
|
0.0526
|
0.1846
|
0.8032
|
0.20
|
|
mFriedman
|
0.0046
|
0.0000
|
0.0037
|
0.0139
|
0.25
|
|
mIshigami
|
0.6362
|
0.4047
|
0.4883
|
2.6808
|
0.17
|
|
mRef153
|
3.3426
|
11.1727
|
2.3126
|
13.7293
|
0.00
|
|
uDmod1
|
0.0451
|
0.0020
|
0.0364
|
0.1166
|
0.00
|
|
uDmod2
|
0.0435
|
0.0019
|
0.0346
|
0.1072
|
0.00
|
|
uDreyfus1
|
0.0025
|
0.0000
|
0.0019
|
0.0082
|
0.00
|
|
uDreyfus2
|
0.0913
|
0.0083
|
0.0730
|
0.2241
|
0.00
|
|
uGauss1
|
2.2526
|
5.0741
|
1.7489
|
7.2646
|
0.01
|
|
uGauss2
|
2.3781
|
5.6555
|
1.8657
|
7.3796
|
0.01
|
|
uGauss3
|
2.4917
|
6.2084
|
1.9659
|
7.4363
|
0.01
|
|
uNeuroOne
|
0.3521
|
0.1240
|
0.2848
|
0.8233
|
0.00
|
kable(t(apply(res, c(1,4), median)))%>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed"))
|
|
RMSE
|
MSE
|
MAE
|
WAE
|
time
|
|
mDette
|
1.50465
|
2.26530
|
1.28140
|
4.95405
|
0.220
|
|
mFriedman
|
0.00495
|
0.00000
|
0.00400
|
0.01480
|
0.270
|
|
mIshigami
|
0.66595
|
0.44345
|
0.51150
|
2.93530
|
0.260
|
|
mRef153
|
3.47940
|
12.10650
|
2.50675
|
14.12545
|
0.020
|
|
uDmod1
|
0.04510
|
0.00200
|
0.03640
|
0.11670
|
0.005
|
|
uDmod2
|
0.06435
|
0.00415
|
0.05310
|
0.14720
|
0.020
|
|
uDreyfus1
|
0.00560
|
0.00000
|
0.00455
|
0.01520
|
0.000
|
|
uDreyfus2
|
0.09130
|
0.00830
|
0.07300
|
0.22410
|
0.000
|
|
uGauss1
|
2.41650
|
5.83930
|
1.88140
|
8.39890
|
0.035
|
|
uGauss2
|
2.37810
|
5.65550
|
1.86570
|
7.37970
|
0.045
|
|
uGauss3
|
3.01195
|
9.10600
|
2.36310
|
8.83710
|
0.030
|
|
uNeuroOne
|
0.35230
|
0.12410
|
0.28480
|
0.82700
|
0.000
|