Dedicated functions by packages
brnn Train/predict Function - arguments x,y
#library(brnn)
brnn.method <- "gaussNewton"
hyperParams.brnn <- function(optim_method, ...) {
if (!is.element(optim_method, c("gaussNewton"))) stop("Invalid Parameters.")
iter <- 200
params <- paste0("method=", optim_method, "_iter=", iter)
out <- list(iter = iter, params = params)
return (out)
}
NNtrain.brnn <- function(x, y, dataxy, formula, neur, optim_method, hyperParams,...) {
hyper_params <- do.call(hyperParams.brnn, list(brnn.method))
iter <- hyper_params$iter
NNreg <- brnn::brnn(x, y, neur, normalize = FALSE, epochs = iter, verbose = FALSE)
return (NNreg)
}
NNpredict.brnn <- function(object, x, ...)
predict(object, x)
NNclose.brnn <- function()
if("package:brnn" %in% search())
detach("package:brnn", unload=TRUE)
brnn.prepareZZ <- list(xdmv = "m", ydmv = "v", zdm = "d", scale = TRUE)
if(FALSE)
res <- trainPredict_1data(1, brnn.method, "NNtrain.brnn", "hyperParams.brnn", "NNpredict.brnn",
NNsummary, "NNclose.brnn", NA, brnn.prepareZZ, nrep=5, echo=TRUE, doplot=FALSE,
pkgname="brnn", pkgfun="brnn", csvfile=TRUE, rdafile=TRUE, odir=odir)
CaDENCE Train/predict Function - arguments x,y
#library(CaDENCE)
CaDENCE.method <- c("optim", "psoptim", "Rprop")
hyperParams.CaDENCE <- function(optim_method, ...) {
if (optim_method == "optim") {iter <- 200}
if (optim_method == "psoptim") {iter <- 1000}
if (optim_method == "Rprop") {iter <- 1000}
params <- paste0("method=", optim_method, "_iter=", iter)
out <- list(iter = iter, method = optim_method, params = params, maxit.Nelder=1)
return (out)
}
NNtrain.CaDENCE <- function(x, y, dataxy, formula, neur, optim_method, hyperParams,...) {
hyper_params <- do.call(hyperParams, list(optim_method, ...))
iter <- hyper_params$iter
method <- hyper_params$method
NNreg <- CaDENCE::cadence.fit(x = x, y = y,
iter.max = iter,
n.hidden = neur,
hidden.fcn = tanh,
method = method,
n.trials = 1,
trace = 0,
maxit.Nelder = 1,
f.cost = cadence.cost,
distribution = list(density.fcn = dnorm,
parameters = c("mean", "sd"),
parameters.fixed = NULL,
output.fcns = c(identity, exp)))
return (NNreg)
}
NNpredict.CaDENCE <- function(object, x, ...)
CaDENCE::cadence.predict(x = x, fit = object)[,1]
NNclose.CaDENCE <- function()
if("package:CaDENCE" %in% search())
detach("package:CaDENCE", unload=TRUE)
CaDENCE.prepareZZ <- list(xdmv = "m", ydmv = "m", zdm = "d", scale = TRUE)
if(FALSE)
res <- trainPredict_1data(1, CaDENCE.method, "NNtrain.CaDENCE", "hyperParams.CaDENCE", "NNpredict.CaDENCE",
NNsummary, "NNclose.CaDENCE", NA, CaDENCE.prepareZZ, nrep=2, echo=TRUE, doplot=FALSE,
pkgname="CaDENCE", pkgfun="cadence.fit", csvfile=TRUE, rdafile=TRUE, odir=odir)
MachineShop Train/predict Functions - arguments formula,data
#library(MachineShop)
MachineShop.method <- "none"
hyperParams.MachineShop <- function(...) {
return (list(iter=200, trace=FALSE, linout=TRUE))
}
NNtrain.MachineShop <- function(x, y, dataxy, formula, neur, method, hyperParams, ...) {
hyper_params <- do.call(hyperParams, list(...))
trace <- hyper_params$trace
maxit <- hyper_params$iter
linout <- hyper_params$linout #linearoutpputunit
myNN <- MachineShop::NNetModel(size = neur, linout = linout, maxit = maxit,
trace=trace)
MachineShop::fit(formula, data = dataxy, model = myNN)
}
NNpredict.MachineShop <- function(object, x, ...)
as.numeric(predict(object, newdata=x, type="response"))
NNclose.MachineShop <- function()
if("package:MachineShop" %in% search())
detach("package:MachineShop", unload=TRUE)
MachineShop.prepareZZ <- list(xdmv = "m", ydmv = "m", zdm = "d", scale = TRUE)
if(FALSE)
res <- trainPredict_1data(1, MachineShop.method, "NNtrain.MachineShop", "hyperParams.MachineShop", "NNpredict.MachineShop",
NNsummary, "NNclose.MachineShop", NA, MachineShop.prepareZZ, nrep=5,
echo=TRUE, doplot=FALSE, echoreport=0,
pkgname="MachineShop", pkgfun="fit", csvfile=TRUE, rdafile=TRUE, odir=odir)
minpack.lm train/predict functions - arguments formula,data
#library(minpack.lm)
minpack.lm.method <- "none"
hyperParams.minpack.lm <- function(...) {
return (list(iter=200, sdnormstart=0.1))
}
NNtrain.minpack.lm <- function(x, y, dataxy, formula, neur, method, hyperParams, NNfullformula, NNparam, ...) {
hyper_params <- do.call(hyperParams, list(...))
start <- round(rnorm(NNparam, sd = hyper_params$sdnormstart), 4)
names(start) <- paste0("b", 1:NNparam)
minpack.lm::nlsLM(NNfullformula, data = dataxy, start=start,
control = list(maxiter = hyper_params$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)
monmlp train/predict functions - arguments x,y
#library(monmlp)
monmlp.method <- c("BFGS", "Nelder-Mead")
hyperParams.monmlp <- function(...) {
return (list(iter=200, silent=TRUE, scale=TRUE))
}
NNtrain.monmlp <- function(x, y, dataxy, formula, neur, method, hyperParams, ...) {
hyper_params <- do.call(hyperParams, list(...))
monmlp::monmlp.fit(x, y, hidden1 = neur, scale.y = hyper_params$scale, silent=hyper_params$silent,
method = method, iter.max = hyper_params$iter)
}
NNpredict.monmlp <- function(object, x, ...)
as.numeric(monmlp::monmlp.predict(x, weights=object))
NNclose.monmlp <- function()
if("package:monmlp" %in% search())
detach("package:monmlp", unload=TRUE)
monmlp.prepareZZ <- list(xdmv = "m", ydmv = "m", zdm = "d", scale = TRUE)
if(FALSE)
res <- trainPredict_1data(1, monmlp.method, "NNtrain.monmlp", "hyperParams.monmlp", "NNpredict.monmlp",
NNsummary, "NNclose.monmlp", NA, monmlp.prepareZZ, nrep=2,
echo=TRUE, doplot=FALSE, echoreport=0,
pkgname="monmlp", pkgfun="monmlp.fit", csvfile=TRUE, rdafile=TRUE, odir=odir)
nlsr train/predict functions - arguments formula,data
#library(nlsr)
nlsr.method <- "none"
hyperParams.nlsr <- function(...) {
return (list(iter=200, sdnormstart=0.1))
}
NNtrain.nlsr <- function(x, y, dataxy, formula, neur, method, hyperParams, NNfullformula, NNparam, ...) {
hyper_params <- do.call(hyperParams, list(...))
start <- round(rnorm(NNparam, sd = hyper_params$sdnormstart), 4)
names(start) <- paste0("b", 1:NNparam)
nlsr::nlxb(NNfullformula, start = start, data = dataxy,
control = list(femax = hyper_params$iter))
}
NNpredict.nlsr <- function(object, x, ...)
as.numeric(predict(object, x))
NNclose.nlsr <- function()
if("package:nlsr" %in% search())
detach("package:nlsr", unload=TRUE)
nlsr.prepareZZ <- list(xdmv = "d", ydmv = "v", zdm = "d", scale = TRUE)
if(FALSE)
res <- trainPredict_1data(1, nlsr.method, "NNtrain.nlsr", "hyperParams.nlsr", "NNpredict.nlsr",
NNsummary, "NNclose.nlsr", NA, nlsr.prepareZZ, nrep=5,
echo=TRUE, doplot=FALSE, echoreport=0,
pkgname="nlsr", pkgfun="nlxb", csvfile=TRUE, rdafile=TRUE, odir=odir)
nnet Train/predict Functions - arguments x,y
#library(nnet)
nnet.method <- "none"
hyperParams.nnet <- function(...) {
return (list(iter=200, trace=FALSE))
}
NNtrain.nnet <- function(x, y, dataxy, formula, neur, method, hyperParams, ...) {
hyper_params <- do.call(hyperParams, list(...))
nnet::nnet(x, y, size = neur, linout = TRUE, maxit = hyper_params$iter, trace=hyper_params$trace)
}
NNpredict.nnet <- function(object, x, ...)
predict(object, newdata=x)
NNclose.nnet <- function()
if("package:nnet" %in% search())
detach("package:nnet", unload=TRUE)
nnet.prepareZZ <- list(xdmv = "d", ydmv = "v", zdm = "d", scale = TRUE)
if(FALSE)
res <- trainPredict_1data(1, nnet.method, "NNtrain.nnet", "hyperParams.nnet", "NNpredict.nnet",
NNsummary, "NNclose.nnet", NA, nnet.prepareZZ, nrep=5, echo=TRUE, doplot=FALSE,
pkgname="nnet", pkgfun="nnet", csvfile=TRUE, rdafile=TRUE, odir=odir)
qrnn Train/predict Function - arguments x,y
#library(qrnn)
qrnn.method <- "none"
hyperParams.qrnn <- function(optim_method, ...) {
maxiter <- 200
init.range = c(-0.1, 0.1, -0.1, 0.1)
params <- paste0("method=", optim_method, "_iter=", maxiter)
out <- list(iter = maxiter, params = params, init.range=init.range)
return (out)
}
NNtrain.qrnn <- function(x, y, dataxy, formula, neur, optim_method, hyperParams,...) {
hyper_params <- do.call(hyperParams, list(optim_method, ...))
NNreg <- qrnn::qrnn.fit(x, y, n.hidden = neur,
iter.max = hyper_params$iter, n.trials = 1,
init.range = hyper_params$init.range, trace=FALSE)
return (NNreg)
}
NNpredict.qrnn <- function(object, x, ...)
qrnn::qrnn.predict(x, object)
NNclose.qrnn <- function()
if("package:qrnn" %in% search())
detach("package:qrnn", unload=TRUE)
qrnn.prepareZZ <- list(xdmv = "m", ydmv = "m", zdm = "d", scale = TRUE)
if(FALSE)
res <- trainPredict_1data(1, qrnn.method, "NNtrain.qrnn", "hyperParams.qrnn", "NNpredict.qrnn",
NNsummary, "NNclose.qrnn", NA, qrnn.prepareZZ, nrep=5, echo=TRUE, doplot=FALSE,
pkgname="qrnn", pkgfun="qrnn.fit", csvfile=TRUE, rdafile=TRUE, odir=odir)
radiant.model Train/predict Functions - arguments xy,y
#library(radiant.model)
radiant.model.method <- "none"
hyperParams.radiant.model <- function(...) {
return (list(type="regression", decay=0))
}
NNtrain.radiant.model <- function(x, y, dataxy, formula, neur, method, hyperParams, ...) {
hyper_params <- do.call(hyperParams, list(...))
radiant.model::nn(dataxy, rvar = "y", evar = attr(terms(formula), "term.labels"),
type = hyper_params$type, size = neur,
decay = hyper_params$decay)
}
NNpredict.radiant.model <- function(object, x, ...)
predict(object, pred_data=as.data.frame(x))$Prediction
NNclose.radiant.model <- function()
if("package:radiant.model" %in% search())
detach("package:radiant.model", unload=TRUE)
radiant.model.prepareZZ <- list(xdmv = "m", ydmv = "m", zdm = "d", scale = TRUE)
if(FALSE)
res <- trainPredict_1data(1, radiant.model.method, "NNtrain.radiant.model", "hyperParams.radiant.model", "NNpredict.radiant.model",
NNsummary, "NNclose.radiant.model", NA, radiant.model.prepareZZ, nrep=5,
echo=TRUE, doplot=FALSE, echoreport=0,
pkgname="radiant.model", pkgfun="nn", csvfile=TRUE, rdafile=TRUE, odir=odir)
RSNNS Train/predict Function - arguments x,y
#library(RSNNS)
RSNNS.method <- c("Rprop","BackpropBatch","BackpropChunk","BackpropMomentum",
"BackpropWeightDecay","Quickprop","SCG","Std_Backpropagation")
hyperParams.RSNNS <- function(optim_method, ...) {
if(optim_method %in% c("Rprop","BackpropChunk","BackpropMomentum","BackpropWeightDecay","SCG","Std_Backpropagation"))
maxiter <- 1000
else
maxiter <- 10000
params <- paste0("method=", optim_method, "_iter=", maxiter)
out <- list(iter = maxiter, sdnormstart=0.1)
return (out)
}
NNtrain.RSNNS <- function(x, y, dataxy, formula, neur, optim_method, hyperParams, NNfullformula, NNparam,...) {
hyper_params <- do.call(hyperParams, list(optim_method, ...))
start <- round(rnorm(NNparam, sd = hyper_params$sdnormstart), 4)
names(start) <- paste0("b", 1:NNparam)
NNreg <- RSNNS::mlp(x, y, initFuncParams = start,
size = neur, learnFunc = optim_method,
maxit = hyper_params$iter, linOut = TRUE)
return (NNreg)
}
NNpredict.RSNNS <- function(object, x, ...)
predict(object, x)
NNclose.RSNNS <- function()
if("package:RSNNS" %in% search())
detach("package:RSNNS", unload=TRUE)
RSNNS.prepareZZ <- list(xdmv = "m", ydmv = "m", zdm = "d", scale = TRUE)
if(FALSE)
res <- trainPredict_1data(1, RSNNS.method, "NNtrain.RSNNS", "hyperParams.RSNNS", "NNpredict.RSNNS",
NNsummary, "NNclose.RSNNS", NA, RSNNS.prepareZZ, nrep=5, echo=TRUE, doplot=FALSE,
pkgname="RSNNS", pkgfun="mlp", csvfile=TRUE, rdafile=TRUE, odir=odir)
validann Train/predict Function - arguments x,y
#library(validann)
validann.method <- c("Nelder-Mead", "BFGS", "CG", "L-BFGS-B", "SANN")
hyperParams.validann <- function(optim_method, ...) {
if (optim_method == "Nelder-Mead") {maxiter <- 10000}
if (optim_method == "BFGS") {maxiter <- 200}
if (optim_method == "CG") {maxiter <- 1000}
if (optim_method == "L-BFGS-B") {maxiter <- 200}
if (optim_method == "SANN") {maxiter <- 10000}
params <- paste0("method=", optim_method, "_iter=", maxiter)
out <- list(iter = maxiter, method = optim_method, params)
return (out)
}
NNtrain.validann <- function(x, y, dataxy, formula, neur, optim_method, hyperParams, NNfullformula, NNparam,...) {
hyper_params <- do.call(hyperParams, list(optim_method, ...))
iter <- hyper_params$iter
method <- hyper_params$method
NNreg <- validann::ann(x, y, size = neur,
method = method, maxit = iter)
return (NNreg)
}
NNpredict.validann <- function(object, x, ...)
predict(object, x)
NNclose.validann <- function()
if("package:validann" %in% search())
detach("package:validann", unload=TRUE)
validann.prepareZZ <- list(xdmv = "m", ydmv = "m", zdm = "d", scale = TRUE)
if(FALSE)
res <- trainPredict_1data(1, validann.method, "NNtrain.validann", "hyperParams.validann", "NNpredict.validann",
NNsummary, "NNclose.validann", NA, validann.prepareZZ, nrep=5, echo=TRUE, doplot=FALSE,
pkgname="validann", pkgfun="ann", csvfile=TRUE, rdafile=TRUE, odir=odir)
Launch all packages
methodlist <- list(brnn.method,
CaDENCE.method[1],
MachineShop.method,
minpack.lm.method,
monmlp.method[1],
nlsr.method,
nnet.method,
qrnn.method,
radiant.model.method,
rminer.method,
validann.method[c(2,4)])
pkgfunmat <- rbind(c("brnn", "brnn"),
c("CaDENCE", "cadence.fit"),
c("MachineShop", "fit"),
c("minpack.lm", "nlsLM"),
c("monmlp", "monmlp.fit"),
c("nlsr", "nlxb"),
c("nnet", "nnet"),
c("qrnn", "qrnn.fit"),
c("radiant.model", "nn"),
c("rminer", "fit"),
c("validann","ann"))
colnames(pkgfunmat) <- c("pkg", "fun")
trainvect <- paste("NNtrain", pkgfunmat[,"pkg"], sep=".")
hypervect <- paste("hyperParams", pkgfunmat[,"pkg"], sep=".")
predvect <- paste("NNpredict", pkgfunmat[,"pkg"], sep=".")
#close function is only needed for h2o
closevect <- paste("NNclose", pkgfunmat[,"pkg"], sep=".")
startvect <- rep(NA, length(pkgfunmat[,"pkg"]))
startvect[pkgfunmat[,"pkg"] == "h2o"] <- "NNstart.h2o"
preparelist <- list(brnn.prepareZZ,
CaDENCE.prepareZZ,
MachineShop.prepareZZ,
minpack.lm.prepareZZ,
monmlp.prepareZZ,
nlsr.prepareZZ,
nnet.prepareZZ,
qrnn.prepareZZ,
radiant.model.prepareZZ,
rminer.prepareZZ,
validann.prepareZZ)
names(preparelist) <- pkgfunmat[,"pkg"]
#print(cbind(pkgfunmat, startvect))
resall <- lapply(1:12, function(i)
trainPredict_1data(dset=i, method=methodlist, train=trainvect, hyper=hypervect,
pred=predvect, summary=NNsummary, close=closevect,
start=startvect, prepare=preparelist, nrep=20, echo=TRUE, doplot=TRUE,
pkgname=pkgfunmat[,"pkg"], pkgfun=pkgfunmat[,"fun"], csvfile=TRUE, rdafile=TRUE, odir=odir))
## ________________________________________________________________________________
## *** mDette_brnn::brnn_gaussNewton ***
## Number of parameters (weights and biases) to estimate: 25
## Nguyen-Widrow method
## Scaling factor= 0.7022568
## gamma= 22.9527 alpha= 0.0024 beta= 15.4661
## Number of parameters (weights and biases) to estimate: 25
## Nguyen-Widrow method
## Scaling factor= 0.7022568
## gamma= 24.659 alpha= 0.0372 beta= 43.5237
## Number of parameters (weights and biases) to estimate: 25
## Nguyen-Widrow method
## Scaling factor= 0.7022568
## gamma= 22.9594 alpha= 0.0027 beta= 15.3467
## Number of parameters (weights and biases) to estimate: 25
## Nguyen-Widrow method
## Scaling factor= 0.7022568
## gamma= 21.6791 alpha= 0.0926 beta= 7.5765
## Number of parameters (weights and biases) to estimate: 25
## Nguyen-Widrow method
## Scaling factor= 0.7022568
## gamma= 22.9531 alpha= 0.0024 beta= 15.4707
## brnn brnn gaussNewton i 5 summary statistics 1.4353 2.0601 1.2306 4.1934 time 0.24
## Number of parameters (weights and biases) to estimate: 25
## Nguyen-Widrow method
## Scaling factor= 0.7022568
## gamma= 24.4559 alpha= 0.0036 beta= 602.0185
## Number of parameters (weights and biases) to estimate: 25
## Nguyen-Widrow method
## Scaling factor= 0.7022568
## gamma= 12.6152 alpha= 0.1772 beta= 2.1215
## Number of parameters (weights and biases) to estimate: 25
## Nguyen-Widrow method
## Scaling factor= 0.7022568
## gamma= 22.9641 alpha= 0.0025 beta= 15.4233
## Number of parameters (weights and biases) to estimate: 25
## Nguyen-Widrow method
## Scaling factor= 0.7022568
## gamma= 21.4224 alpha= 0.086 beta= 7.6361
## Number of parameters (weights and biases) to estimate: 25
## Nguyen-Widrow method
## Scaling factor= 0.7022568
## gamma= 21.9962 alpha= 0.0595 beta= 8.2174
## brnn brnn gaussNewton i 10 summary statistics 1.9714 3.8864 1.4556 12.0568 time 0.21
## Number of parameters (weights and biases) to estimate: 25
## Nguyen-Widrow method
## Scaling factor= 0.7022568
## gamma= 21.6098 alpha= 0.0897 beta= 7.5953
## Number of parameters (weights and biases) to estimate: 25
## Nguyen-Widrow method
## Scaling factor= 0.7022568
## gamma= 22.0144 alpha= 0.0549 beta= 8.356
## Number of parameters (weights and biases) to estimate: 25
## Nguyen-Widrow method
## Scaling factor= 0.7022568
## gamma= 24.4734 alpha= 0.0027 beta= 632.0584
## Number of parameters (weights and biases) to estimate: 25
## Nguyen-Widrow method
## Scaling factor= 0.7022568
## gamma= 24.6962 alpha= 0.0329 beta= 44.0271
## Number of parameters (weights and biases) to estimate: 25
## Nguyen-Widrow method
## Scaling factor= 0.7022568
## gamma= 22.9824 alpha= 0.0029 beta= 15.2506
## brnn brnn gaussNewton i 15 summary statistics 1.4456 2.0897 1.2374 4.3071 time 0.22
## Number of parameters (weights and biases) to estimate: 25
## Nguyen-Widrow method
## Scaling factor= 0.7022568
## gamma= 24.484 alpha= 0.0243 beta= 145.3324
## Number of parameters (weights and biases) to estimate: 25
## Nguyen-Widrow method
## Scaling factor= 0.7022568
## gamma= 21.7584 alpha= 0.0618 beta= 8.1654
## Number of parameters (weights and biases) to estimate: 25
## Nguyen-Widrow method
## Scaling factor= 0.7022568
## gamma= 21.5612 alpha= 0.0866 beta= 7.6149
## Number of parameters (weights and biases) to estimate: 25
## Nguyen-Widrow method
## Scaling factor= 0.7022568
## gamma= 24.4636 alpha= 0.0036 beta= 600.7179
## Number of parameters (weights and biases) to estimate: 25
## Nguyen-Widrow method
## Scaling factor= 0.7022568
## gamma= 21.5136 alpha= 0.0879 beta= 7.599
## brnn brnn gaussNewton i 20 summary statistics 2.0511 4.2069 1.4612 12.5268 time 0.2
##
## ________________________________________________________________________________
## *** mDette_CaDENCE::cadence.fit_optim ***
## n.hidden = 5 --> 1 * NLL = -741.3285 ; penalty = 0; BIC = -1283.789 ; AICc = -1414.134 ; AIC = -1418.657
## n.hidden = 5 --> 1 * NLL = -486.8946 ; penalty = 0; BIC = -774.9216 ; AICc = -905.2666 ; AIC = -909.7891
## n.hidden = 5 --> 1 * NLL = -750.4774 ; penalty = 0; BIC = -1302.087 ; AICc = -1432.432 ; AIC = -1436.955
## n.hidden = 5 --> 1 * NLL = -820.129 ; penalty = 0; BIC = -1441.391 ; AICc = -1571.735 ; AIC = -1576.258
## n.hidden = 5 --> 1 * NLL = -761.5089 ; penalty = 0; BIC = -1324.15 ; AICc = -1454.495 ; AIC = -1459.018
## CaDENCE cadence.fit optim i 5 summary statistics 3.8464 14.7949 1.6238 21.6959 time 7.29
## n.hidden = 5 --> 1 * NLL = -582.4775 ; penalty = 0; BIC = -966.0875 ; AICc = -1096.432 ; AIC = -1100.955
## n.hidden = 5 --> 1 * NLL = -819.1573 ; penalty = 0; BIC = -1439.447 ; AICc = -1569.792 ; AIC = -1574.315
## n.hidden = 5 --> 1 * NLL = -587.856 ; penalty = 0; BIC = -976.8446 ; AICc = -1107.19 ; AIC = -1111.712
## n.hidden = 5 --> 1 * NLL = -810.9247 ; penalty = 0; BIC = -1422.982 ; AICc = -1553.327 ; AIC = -1557.849
## n.hidden = 5 --> 1 * NLL = -997.8195 ; penalty = 0; BIC = -1796.771 ; AICc = -1927.116 ; AIC = -1931.639
## CaDENCE cadence.fit optim i 10 summary statistics 0.5256 0.2762 0.3168 3.1999 time 7.19
## n.hidden = 5 --> 1 * NLL = -478.8517 ; penalty = 0; BIC = -758.836 ; AICc = -889.181 ; AIC = -893.7035
## n.hidden = 5 --> 1 * NLL = -746.8115 ; penalty = 0; BIC = -1294.756 ; AICc = -1425.101 ; AIC = -1429.623
## n.hidden = 5 --> 1 * NLL = -757.8577 ; penalty = 0; BIC = -1316.848 ; AICc = -1447.193 ; AIC = -1451.715
## n.hidden = 5 --> 1 * NLL = -1022.13 ; penalty = 0; BIC = -1845.392 ; AICc = -1975.737 ; AIC = -1980.259
## n.hidden = 5 --> 1 * NLL = -768.3174 ; penalty = 0; BIC = -1337.767 ; AICc = -1468.112 ; AIC = -1472.635
## CaDENCE cadence.fit optim i 15 summary statistics 2.7686 7.6649 1.1671 16.9759 time 7.22
## n.hidden = 5 --> 1 * NLL = -689.9055 ; penalty = 0; BIC = -1180.944 ; AICc = -1311.289 ; AIC = -1315.811
## n.hidden = 5 --> 1 * NLL = -954.3794 ; penalty = 0; BIC = -1709.891 ; AICc = -1840.236 ; AIC = -1844.759
## n.hidden = 5 --> 1 * NLL = -898.4695 ; penalty = 0; BIC = -1598.071 ; AICc = -1728.416 ; AIC = -1732.939
## n.hidden = 5 --> 1 * NLL = -864.0685 ; penalty = 0; BIC = -1529.27 ; AICc = -1659.615 ; AIC = -1664.137
## n.hidden = 5 --> 1 * NLL = -988.9547 ; penalty = 0; BIC = -1779.042 ; AICc = -1909.387 ; AIC = -1913.909
## CaDENCE cadence.fit optim i 20 summary statistics 0.5341 0.2853 0.3201 3.1034 time 7.23
##
## ________________________________________________________________________________
## *** mDette_MachineShop::fit_none ***
## MachineShop fit none i 5 summary statistics 0.5037 0.2537 0.3773 1.9984 time 0.07
## MachineShop fit none i 10 summary statistics 0.3194 0.102 0.2466 1.2523 time 0.1
## MachineShop fit none i 15 summary statistics 0.6898 0.4759 0.5626 3.1425 time 0.08
## MachineShop fit none i 20 summary statistics 0.3691 0.1362 0.2854 1.8224 time 0.07
##
## ________________________________________________________________________________
## *** mDette_minpack.lm::nlsLM_none ***
## minpack.lm nlsLM none i 5 summary statistics 0.1635 0.0267 0.1256 0.7672 time 0.22
## minpack.lm nlsLM none i 10 summary statistics 0.199 0.0396 0.1608 0.6348 time 0.25
## minpack.lm nlsLM none i 15 summary statistics 0.3526 0.1243 0.2714 1.3262 time 0.25
## minpack.lm nlsLM none i 20 summary statistics 0.3523 0.1241 0.2713 1.3253 time 0.25
##
## ________________________________________________________________________________
## *** mDette_monmlp::monmlp.fit_BFGS ***
## monmlp monmlp.fit BFGS i 5 summary statistics 0.4585 0.2102 0.3535 2.0194 time 0.25
## monmlp monmlp.fit BFGS i 10 summary statistics 0.7607 0.5786 0.5664 4.8692 time 0.25
## monmlp monmlp.fit BFGS i 15 summary statistics 0.5912 0.3495 0.447 2.4778 time 0.25
## monmlp monmlp.fit BFGS i 20 summary statistics 0.3853 0.1485 0.2835 1.684 time 0.24
##
## ________________________________________________________________________________
## *** mDette_nlsr::nlxb_none ***
## 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
## nlsr nlxb none i 5 summary statistics 1.4488 2.099 1.2517 4.1423 time 0.42
## 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
## nlsr nlxb none i 10 summary statistics 1.3497 1.8217 1.1755 3.4845 time 0.42
## 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
## nlsr nlxb none i 15 summary statistics 0.439 0.1927 0.3398 2.5709 time 0.41
## 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
## nlsr nlxb none i 20 summary statistics 1.487 2.2111 1.2738 4.6121 time 0.42
##
## ________________________________________________________________________________
## *** mDette_nnet::nnet_none ***
## nnet nnet none i 5 summary statistics 0.4667 0.2178 0.3464 2.0191 time 0.06
## nnet nnet none i 10 summary statistics 0.6074 0.3689 0.482 3.5417 time 0.08
## nnet nnet none i 15 summary statistics 0.751 0.564 0.6004 4.8829 time 0.08
## nnet nnet none i 20 summary statistics 0.5408 0.2925 0.4224 2.3817 time 0.06
##
## ________________________________________________________________________________
## *** mDette_qrnn::qrnn.fit_none ***
## qrnn qrnn.fit none i 5 summary statistics 0.3331 0.111 0.2254 1.6869 time 0.83
## qrnn qrnn.fit none i 10 summary statistics 2.2302 4.9738 1.0989 15.3555 time 0.27
## qrnn qrnn.fit none i 15 summary statistics 2.4759 6.1302 1.1714 16.674 time 0.39
## qrnn qrnn.fit none i 20 summary statistics 0.3479 0.1211 0.2302 1.6711 time 0.72
##
## ________________________________________________________________________________
## *** mDette_radiant.model::nn_none ***
## radiant.model nn none i 5 summary statistics 0.2941 0.0865 0.2275 1.2431 time 0.09
## radiant.model nn none i 10 summary statistics 0.2847 0.081 0.2215 1.2342 time 0.09
## radiant.model nn none i 15 summary statistics 0.3537 0.1251 0.2722 1.3258 time 0.09
## radiant.model nn none i 20 summary statistics 0.3635 0.1321 0.2812 1.5104 time 0.11
##
## ________________________________________________________________________________
## *** mDette_rminer::fit_none ***
## rminer fit none i 5 summary statistics 0.2192 0.0481 0.1717 1.0605 time 0.26
## rminer fit none i 10 summary statistics 0.4542 0.2063 0.3427 1.8491 time 0.24
## rminer fit none i 15 summary statistics 0.4814 0.2318 0.3629 2.0559 time 0.25
## rminer fit none i 20 summary statistics 0.6042 0.365 0.4944 3.1704 time 0.27
##
## ________________________________________________________________________________
## *** mDette_validann::ann_BFGS ***
## initial value 586.848863
## iter 20 value 154.828686
## iter 40 value 61.828500
## iter 60 value 42.108613
## iter 80 value 36.152415
## iter 100 value 31.958801
## iter 120 value 30.488794
## iter 140 value 6.881132
## iter 160 value 2.314412
## iter 180 value 2.265465
## iter 200 value 2.129791
## final value 2.129791
## stopped after 200 iterations
## initial value 541.130973
## iter 20 value 167.102934
## iter 40 value 45.740764
## iter 60 value 35.966980
## iter 80 value 19.841864
## iter 100 value 2.640803
## iter 120 value 1.522857
## iter 140 value 1.090718
## iter 160 value 0.815782
## iter 180 value 0.781932
## iter 200 value 0.608434
## final value 0.608434
## stopped after 200 iterations
## initial value 678.483947
## iter 20 value 101.843848
## iter 40 value 41.263917
## iter 60 value 34.786464
## iter 80 value 31.823000
## iter 100 value 26.071368
## iter 120 value 22.855798
## iter 140 value 20.955814
## iter 160 value 15.778356
## iter 180 value 12.551254
## iter 200 value 7.308888
## final value 7.308888
## stopped after 200 iterations
## initial value 508.813999
## iter 20 value 112.515957
## iter 40 value 42.101506
## iter 60 value 37.387069
## iter 80 value 28.386522
## iter 100 value 12.856229
## iter 120 value 9.903434
## iter 140 value 6.937385
## iter 160 value 6.199495
## iter 180 value 5.718872
## iter 200 value 4.358715
## final value 4.358715
## stopped after 200 iterations
## initial value 565.887988
## iter 20 value 92.824995
## iter 40 value 43.201856
## iter 60 value 38.648958
## iter 80 value 36.129498
## iter 100 value 32.848162
## iter 120 value 32.305937
## iter 140 value 26.660093
## iter 160 value 3.582768
## iter 180 value 2.686375
## iter 200 value 2.610324
## final value 2.610324
## stopped after 200 iterations
## validann ann BFGS i 5 summary statistics 0.5906 0.3488 0.4575 2.3016 time 1.72
## initial value 500.673212
## iter 20 value 131.185987
## iter 40 value 45.432036
## iter 60 value 37.882154
## iter 80 value 34.296459
## iter 100 value 29.897307
## iter 120 value 28.819222
## iter 140 value 28.139561
## iter 160 value 27.442995
## iter 180 value 27.291929
## iter 200 value 27.036714
## final value 27.036714
## stopped after 200 iterations
## initial value 508.533462
## iter 20 value 183.121123
## iter 40 value 46.550097
## iter 60 value 40.390270
## iter 80 value 33.285182
## iter 100 value 4.851141
## iter 120 value 4.122055
## iter 140 value 2.773887
## iter 160 value 2.346666
## iter 180 value 2.198573
## iter 200 value 1.662721
## final value 1.662721
## stopped after 200 iterations
## initial value 482.965560
## iter 20 value 120.072317
## iter 40 value 20.831206
## iter 60 value 5.465474
## iter 80 value 2.303100
## iter 100 value 1.146966
## iter 120 value 1.030473
## iter 140 value 0.854019
## iter 160 value 0.694404
## iter 180 value 0.652186
## iter 200 value 0.582259
## final value 0.582259
## stopped after 200 iterations
## initial value 625.976035
## iter 20 value 131.734377
## iter 40 value 28.800623
## iter 60 value 23.890385
## iter 80 value 11.487167
## iter 100 value 1.683617
## iter 120 value 1.241098
## iter 140 value 0.899844
## iter 160 value 0.739846
## iter 180 value 0.706304
## iter 200 value 0.581686
## final value 0.581686
## stopped after 200 iterations
## initial value 684.810375
## iter 20 value 73.010908
## iter 40 value 40.399117
## iter 60 value 33.768457
## iter 80 value 31.739463
## iter 100 value 25.153262
## iter 120 value 23.519445
## iter 140 value 22.547320
## iter 160 value 20.388856
## iter 180 value 17.991067
## iter 200 value 10.861166
## final value 10.861166
## stopped after 200 iterations
## validann ann BFGS i 10 summary statistics 1.2047 1.4513 0.9615 5.2514 time 1.69
## initial value 547.901396
## iter 20 value 194.304138
## iter 40 value 57.338656
## iter 60 value 45.346168
## iter 80 value 38.209786
## iter 100 value 32.420063
## iter 120 value 29.942889
## iter 140 value 28.517500
## iter 160 value 27.888655
## iter 180 value 27.774586
## iter 200 value 27.525736
## final value 27.525736
## stopped after 200 iterations
## initial value 527.233597
## iter 20 value 256.399464
## iter 40 value 65.376447
## iter 60 value 30.020851
## iter 80 value 6.979550
## iter 100 value 1.909895
## iter 120 value 1.766776
## iter 140 value 1.579791
## iter 160 value 1.492959
## iter 180 value 1.472835
## iter 200 value 1.349587
## final value 1.349587
## stopped after 200 iterations
## initial value 729.687701
## iter 20 value 229.240292
## iter 40 value 69.390761
## iter 60 value 47.414221
## iter 80 value 36.813730
## iter 100 value 31.519923
## iter 120 value 29.010938
## iter 140 value 24.103814
## iter 160 value 7.378191
## iter 180 value 3.562672
## iter 200 value 1.845014
## final value 1.845014
## stopped after 200 iterations
## initial value 481.847244
## iter 20 value 106.061534
## iter 40 value 27.692632
## iter 60 value 21.390435
## iter 80 value 11.186850
## iter 100 value 5.713287
## iter 120 value 4.779614
## iter 140 value 4.022842
## iter 160 value 3.407918
## iter 180 value 3.307776
## iter 200 value 3.023036
## final value 3.023036
## stopped after 200 iterations
## initial value 575.998683
## iter 20 value 155.337445
## iter 40 value 48.884449
## iter 60 value 13.303457
## iter 80 value 4.265997
## iter 100 value 2.564739
## iter 120 value 2.245247
## iter 140 value 1.786216
## iter 160 value 1.013189
## iter 180 value 0.811200
## iter 200 value 0.643566
## final value 0.643566
## stopped after 200 iterations
## validann ann BFGS i 15 summary statistics 0.2932 0.086 0.233 1.2439 time 1.68
## initial value 645.891898
## iter 20 value 268.692771
## iter 40 value 66.044239
## iter 60 value 40.429751
## iter 80 value 35.734371
## iter 100 value 31.231980
## iter 120 value 30.410355
## iter 140 value 29.281323
## iter 160 value 28.147666
## iter 180 value 27.793170
## iter 200 value 27.037233
## final value 27.037233
## stopped after 200 iterations
## initial value 484.930411
## iter 20 value 134.588479
## iter 40 value 18.225944
## iter 60 value 7.874403
## iter 80 value 2.285083
## iter 100 value 1.082660
## iter 120 value 1.029167
## iter 140 value 0.970062
## iter 160 value 0.945433
## iter 180 value 0.943570
## iter 200 value 0.936946
## final value 0.936946
## stopped after 200 iterations
## initial value 655.867291
## iter 20 value 140.954697
## iter 40 value 47.925782
## iter 60 value 38.957566
## iter 80 value 36.561412
## iter 100 value 33.099047
## iter 120 value 32.394262
## iter 140 value 31.840517
## iter 160 value 28.175288
## iter 180 value 27.032785
## iter 200 value 24.968413
## final value 24.968413
## stopped after 200 iterations
## initial value 567.513091
## iter 20 value 202.802735
## iter 40 value 53.026239
## iter 60 value 33.425582
## iter 80 value 27.962695
## iter 100 value 17.936691
## iter 120 value 8.529452
## iter 140 value 7.050089
## iter 160 value 5.399096
## iter 180 value 4.634772
## iter 200 value 4.184234
## final value 4.184234
## stopped after 200 iterations
## initial value 595.175459
## iter 20 value 245.150226
## iter 40 value 51.192523
## iter 60 value 13.900130
## iter 80 value 3.306855
## iter 100 value 1.463393
## iter 120 value 1.277511
## iter 140 value 1.039860
## iter 160 value 0.963897
## iter 180 value 0.960557
## iter 200 value 0.942269
## final value 0.942269
## stopped after 200 iterations
## validann ann BFGS i 20 summary statistics 0.3548 0.1259 0.2728 1.3325 time 1.72
##
## ________________________________________________________________________________
## *** mDette_validann::ann_L-BFGS-B ***
## iter 20 value 68.591904
## iter 40 value 39.473652
## iter 60 value 36.670183
## iter 80 value 33.369457
## iter 100 value 32.697085
## iter 120 value 31.463162
## iter 140 value 30.530856
## iter 160 value 29.522925
## iter 180 value 26.938002
## iter 200 value 16.896935
## final value 15.623465
## stopped after 201 iterations
## iter 20 value 70.771612
## iter 40 value 33.590674
## iter 60 value 9.073756
## iter 80 value 2.431349
## iter 100 value 1.809192
## iter 120 value 1.515843
## iter 140 value 1.338776
## iter 160 value 1.245210
## iter 180 value 1.159453
## iter 200 value 1.114467
## final value 1.113646
## stopped after 201 iterations
## iter 20 value 52.632315
## iter 40 value 39.296340
## iter 60 value 37.434875
## iter 80 value 35.006168
## iter 100 value 32.424117
## iter 120 value 30.100164
## iter 140 value 29.122703
## iter 160 value 28.059483
## iter 180 value 27.622130
## iter 200 value 27.013283
## final value 27.003880
## stopped after 201 iterations
## iter 20 value 59.944645
## iter 40 value 39.286579
## iter 60 value 26.615943
## iter 80 value 7.399104
## iter 100 value 3.184058
## iter 120 value 2.326461
## iter 140 value 1.982822
## iter 160 value 1.787306
## iter 180 value 1.649851
## iter 200 value 1.532539
## final value 1.526991
## stopped after 201 iterations
## iter 20 value 313.801954
## iter 40 value 51.296298
## iter 60 value 13.701516
## iter 80 value 5.874022
## iter 100 value 5.052366
## iter 120 value 4.503229
## iter 140 value 3.872847
## iter 160 value 3.451878
## iter 180 value 3.347552
## iter 200 value 2.909208
## final value 2.875096
## stopped after 201 iterations
## validann ann L-BFGS-B i 5 summary statistics 0.6198 0.3842 0.4542 2.6792 time 1.79
## iter 20 value 90.947176
## iter 40 value 40.490780
## iter 60 value 37.575585
## iter 80 value 34.859675
## iter 100 value 32.621546
## iter 120 value 31.535720
## iter 140 value 30.957483
## iter 160 value 30.542821
## iter 180 value 29.401069
## iter 200 value 27.800547
## final value 27.787330
## stopped after 201 iterations
## iter 20 value 62.803983
## iter 40 value 29.450714
## iter 60 value 9.705550
## iter 80 value 4.127748
## iter 100 value 2.243306
## iter 120 value 1.639541
## iter 140 value 1.429846
## iter 160 value 1.270095
## iter 180 value 1.194086
## iter 200 value 1.161773
## final value 1.157236
## stopped after 201 iterations
## iter 20 value 63.615078
## iter 40 value 42.840904
## iter 60 value 36.395988
## iter 80 value 30.969082
## iter 100 value 8.076561
## iter 120 value 2.927079
## iter 140 value 1.509893
## iter 160 value 1.313448
## iter 180 value 1.185592
## iter 200 value 1.137038
## final value 1.136232
## stopped after 201 iterations
## iter 20 value 80.401422
## iter 40 value 15.350351
## iter 60 value 4.460520
## iter 80 value 2.241854
## iter 100 value 1.709935
## iter 120 value 1.382388
## iter 140 value 1.287581
## iter 160 value 1.217539
## iter 180 value 1.164634
## iter 200 value 1.149906
## final value 1.148913
## stopped after 201 iterations
## iter 20 value 77.388171
## iter 40 value 41.618352
## iter 60 value 23.315830
## iter 80 value 6.207727
## iter 100 value 4.883576
## iter 120 value 4.384430
## iter 140 value 3.632879
## iter 160 value 3.362091
## iter 180 value 3.001747
## iter 200 value 2.939488
## final value 2.937768
## stopped after 201 iterations
## validann ann L-BFGS-B i 10 summary statistics 0.6265 0.3925 0.4823 2.559 time 1.81
## iter 20 value 89.836049
## iter 40 value 23.672965
## iter 60 value 5.949110
## iter 80 value 3.342286
## iter 100 value 2.405043
## iter 120 value 1.860950
## iter 140 value 1.518619
## iter 160 value 1.423598
## iter 180 value 1.397569
## iter 200 value 1.376801
## final value 1.376603
## stopped after 201 iterations
## iter 20 value 143.511011
## iter 40 value 41.533112
## iter 60 value 28.898839
## iter 80 value 11.561117
## iter 100 value 4.234210
## iter 120 value 2.181361
## iter 140 value 1.822172
## iter 160 value 1.523683
## iter 180 value 1.454199
## iter 200 value 1.396295
## final value 1.394270
## stopped after 201 iterations
## iter 20 value 76.852636
## iter 40 value 38.696887
## iter 60 value 34.733505
## iter 80 value 31.795399
## iter 100 value 27.147393
## iter 120 value 15.706016
## iter 140 value 3.813946
## iter 160 value 2.912359
## iter 180 value 2.418900
## iter 200 value 1.819789
## final value 1.800399
## stopped after 201 iterations
## iter 20 value 84.744062
## iter 40 value 38.178916
## iter 60 value 33.489408
## iter 80 value 22.648811
## iter 100 value 17.405610
## iter 120 value 15.273757
## iter 140 value 12.546486
## iter 160 value 10.527091
## iter 180 value 9.276892
## iter 200 value 8.518822
## final value 8.488811
## stopped after 201 iterations
## iter 20 value 126.501550
## iter 40 value 81.633068
## iter 60 value 44.802654
## iter 80 value 39.059324
## iter 100 value 35.093871
## iter 120 value 28.947983
## iter 140 value 26.433257
## iter 160 value 24.794519
## iter 180 value 23.702395
## iter 200 value 21.692441
## final value 21.637155
## stopped after 201 iterations
## validann ann L-BFGS-B i 15 summary statistics 1.7003 2.8912 1.3675 9.0496 time 1.77
## iter 20 value 52.442350
## iter 40 value 36.181467
## iter 60 value 30.928177
## iter 80 value 11.467111
## iter 100 value 4.104325
## iter 120 value 2.563571
## iter 140 value 2.333951
## iter 160 value 1.798609
## iter 180 value 1.620421
## iter 200 value 1.407895
## final value 1.403126
## stopped after 201 iterations
## iter 20 value 61.918870
## iter 40 value 39.310424
## iter 60 value 36.440038
## iter 80 value 33.052496
## iter 100 value 31.026976
## iter 120 value 29.957964
## iter 140 value 28.945857
## iter 160 value 28.571742
## iter 180 value 28.054397
## iter 200 value 27.064653
## final value 27.053261
## stopped after 201 iterations
## iter 20 value 56.898553
## iter 40 value 16.234671
## iter 60 value 5.725280
## iter 80 value 3.396184
## iter 100 value 2.125841
## iter 120 value 1.510716
## iter 140 value 1.181504
## iter 160 value 1.137442
## iter 180 value 1.113855
## iter 200 value 1.087138
## final value 1.086128
## stopped after 201 iterations
## iter 20 value 67.723220
## iter 40 value 43.311724
## iter 60 value 39.279175
## iter 80 value 12.112061
## iter 100 value 4.644063
## iter 120 value 2.942073
## iter 140 value 2.306622
## iter 160 value 1.964874
## iter 180 value 1.678100
## iter 200 value 1.451185
## final value 1.444551
## stopped after 201 iterations
## iter 20 value 70.294146
## iter 40 value 38.643394
## iter 60 value 35.387487
## iter 80 value 33.211554
## iter 100 value 27.803551
## iter 120 value 22.172489
## iter 140 value 13.581892
## iter 160 value 12.290304
## iter 180 value 11.534268
## iter 200 value 11.038398
## final value 11.018117
## stopped after 201 iterations
## validann ann L-BFGS-B i 20 summary statistics 1.2134 1.4723 0.8811 8.5046 time 1.75
##
## ________________________________________________________________________________
## *** mFriedman_brnn::brnn_gaussNewton ***
## Number of parameters (weights and biases) to estimate: 35
## Nguyen-Widrow method
## Scaling factor= 0.7022568
## gamma= 33.4483 alpha= 0.0031 beta= 1220.222
## Number of parameters (weights and biases) to estimate: 35
## Nguyen-Widrow method
## Scaling factor= 0.7022568
## gamma= 32.7849 alpha= 0.5026 beta= 3.9391
## Number of parameters (weights and biases) to estimate: 35
## Nguyen-Widrow method
## Scaling factor= 0.7022568
## gamma= 34.2667 alpha= 0.0707 beta= 93.0582
## Number of parameters (weights and biases) to estimate: 35
## Nguyen-Widrow method
## Scaling factor= 0.7022568
## gamma= 33.4451 alpha= 0.003 beta= 1230.241
## Number of parameters (weights and biases) to estimate: 35
## Nguyen-Widrow method
## Scaling factor= 0.7022568
## gamma= 33.8065 alpha= 0.0022 beta= 833.1359
## brnn brnn gaussNewton i 5 summary statistics 0.0056 0 0.0043 0.0192 time 0.26
## Number of parameters (weights and biases) to estimate: 35
## Nguyen-Widrow method
## Scaling factor= 0.7022568
## gamma= 33.4672 alpha= 0.0032 beta= 1208.985
## Number of parameters (weights and biases) to estimate: 35
## Nguyen-Widrow method
## Scaling factor= 0.7022568
## gamma= 33.5455 alpha= 0.0037 beta= 1162.11
## Number of parameters (weights and biases) to estimate: 35
## Nguyen-Widrow method
## Scaling factor= 0.7022568
## gamma= 33.4725 alpha= 0.0032 beta= 1213.99
## Number of parameters (weights and biases) to estimate: 35
## Nguyen-Widrow method
## Scaling factor= 0.7022568
## gamma= 33.8336 alpha= 0.0021 beta= 830.3436
## Number of parameters (weights and biases) to estimate: 35
## Nguyen-Widrow method
## Scaling factor= 0.7022568
## gamma= 33.4725 alpha= 0.0032 beta= 1213.973
## brnn brnn gaussNewton i 10 summary statistics 0.0046 0 0.0037 0.0141 time 0.27
## Number of parameters (weights and biases) to estimate: 35
## Nguyen-Widrow method
## Scaling factor= 0.7022568
## gamma= 33.6556 alpha= 0.0046 beta= 1104.34
## Number of parameters (weights and biases) to estimate: 35
## Nguyen-Widrow method
## Scaling factor= 0.7022568
## gamma= 33.4606 alpha= 0.0032 beta= 1212.873
## Number of parameters (weights and biases) to estimate: 35
## Nguyen-Widrow method
## Scaling factor= 0.7022568
## gamma= 33.9213 alpha= 0.0076 beta= 936.7774
## Number of parameters (weights and biases) to estimate: 35
## Nguyen-Widrow method
## Scaling factor= 0.7022568
## gamma= 33.4792 alpha= 0.0033 beta= 1209.973
## Number of parameters (weights and biases) to estimate: 35
## Nguyen-Widrow method
## Scaling factor= 0.7022568
## gamma= 33.6742 alpha= 0.0047 beta= 1093.025
## brnn brnn gaussNewton i 15 summary statistics 0.0049 0 0.0039 0.0147 time 0.28
## Number of parameters (weights and biases) to estimate: 35
## Nguyen-Widrow method
## Scaling factor= 0.7022568
## gamma= 33.8053 alpha= 0.0022 beta= 833.395
## Number of parameters (weights and biases) to estimate: 35
## Nguyen-Widrow method
## Scaling factor= 0.7022568
## gamma= 33.6467 alpha= 0.0045 beta= 1100.802
## Number of parameters (weights and biases) to estimate: 35
## Nguyen-Widrow method
## Scaling factor= 0.7022568
## gamma= 33.8065 alpha= 0.0022 beta= 833.1366
## Number of parameters (weights and biases) to estimate: 35
## Nguyen-Widrow method
## Scaling factor= 0.7022568
## gamma= 33.4858 alpha= 0.0033 beta= 1206.08
## Number of parameters (weights and biases) to estimate: 35
## Nguyen-Widrow method
## Scaling factor= 0.7022568
## gamma= 33.4364 alpha= 0.003 beta= 1235.389
## brnn brnn gaussNewton i 20 summary statistics 0.0046 0 0.0037 0.014 time 0.28
##
## ________________________________________________________________________________
## *** mFriedman_CaDENCE::cadence.fit_optim ***
## n.hidden = 5 --> 1 * NLL = -366.1548 ; penalty = 0; BIC = -471.2961 ; AICc = -640.4059 ; AIC = -648.3096
## n.hidden = 5 --> 1 * NLL = -625.8487 ; penalty = 0; BIC = -990.6838 ; AICc = -1159.794 ; AIC = -1167.697
## n.hidden = 5 --> 1 * NLL = 189.7505 ; penalty = 0; BIC = 640.5146 ; AICc = 471.4048 ; AIC = 463.5011
## n.hidden = 5 --> 1 * NLL = -500.5732 ; penalty = 0; BIC = -740.1329 ; AICc = -909.2427 ; AIC = -917.1464
## n.hidden = 5 --> 1 * NLL = 104.4758 ; penalty = 0; BIC = 469.9651 ; AICc = 300.8553 ; AIC = 292.9516
## CaDENCE cadence.fit optim i 5 summary statistics 0.0957 0.0092 0.0684 0.282 time 9.72
## n.hidden = 5 --> 1 * NLL = -406.3417 ; penalty = 0; BIC = -551.6699 ; AICc = -720.7798 ; AIC = -728.6835
## n.hidden = 5 --> 1 * NLL = -426.057 ; penalty = 0; BIC = -591.1005 ; AICc = -760.2104 ; AIC = -768.1141
## n.hidden = 5 --> 1 * NLL = 169.4856 ; penalty = 0; BIC = 599.9848 ; AICc = 430.875 ; AIC = 422.9713
## n.hidden = 5 --> 1 * NLL = -577.0533 ; penalty = 0; BIC = -893.0931 ; AICc = -1062.203 ; AIC = -1070.107
## n.hidden = 5 --> 1 * NLL = -536.3025 ; penalty = 0; BIC = -811.5914 ; AICc = -980.7013 ; AIC = -988.605
## CaDENCE cadence.fit optim i 10 summary statistics 0.0246 0.0006 0.0185 0.0748 time 9.78
## n.hidden = 5 --> 1 * NLL = -352.9352 ; penalty = 0; BIC = -444.8568 ; AICc = -613.9666 ; AIC = -621.8703
## n.hidden = 5 --> 1 * NLL = -405.5917 ; penalty = 0; BIC = -550.1698 ; AICc = -719.2797 ; AIC = -727.1834
## n.hidden = 5 --> 1 * NLL = -330.8096 ; penalty = 0; BIC = -400.6057 ; AICc = -569.7155 ; AIC = -577.6192
## n.hidden = 5 --> 1 * NLL = 180.1217 ; penalty = 0; BIC = 621.257 ; AICc = 452.1472 ; AIC = 444.2435
## n.hidden = 5 --> 1 * NLL = -450.288 ; penalty = 0; BIC = -639.5625 ; AICc = -808.6723 ; AIC = -816.576
## CaDENCE cadence.fit optim i 15 summary statistics 0.098 0.0096 0.0555 0.3035 time 9.74
## n.hidden = 5 --> 1 * NLL = -608.4756 ; penalty = 0; BIC = -955.9376 ; AICc = -1125.047 ; AIC = -1132.951
## n.hidden = 5 --> 1 * NLL = -404.5813 ; penalty = 0; BIC = -548.149 ; AICc = -717.2588 ; AIC = -725.1625
## n.hidden = 5 --> 1 * NLL = -406.2106 ; penalty = 0; BIC = -551.4076 ; AICc = -720.5174 ; AIC = -728.4211
## n.hidden = 5 --> 1 * NLL = -520.4697 ; penalty = 0; BIC = -779.9258 ; AICc = -949.0356 ; AIC = -956.9393
## n.hidden = 5 --> 1 * NLL = 82.82277 ; penalty = 0; BIC = 426.6591 ; AICc = 257.5493 ; AIC = 249.6455
## CaDENCE cadence.fit optim i 20 summary statistics 0.1016 0.0103 0.0692 0.3283 time 9.81
##
## ________________________________________________________________________________
## *** mFriedman_MachineShop::fit_none ***
## MachineShop fit none i 5 summary statistics 0.1007 0.0101 0.0818 0.263 time 0.08
## MachineShop fit none i 10 summary statistics 0.0174 0.0003 0.0139 0.0722 time 0.09
## MachineShop fit none i 15 summary statistics 0.0669 0.0045 0.0561 0.1554 time 0.1
## MachineShop fit none i 20 summary statistics 0.0227 0.0005 0.0174 0.0677 time 0.11
##
## ________________________________________________________________________________
## *** mFriedman_minpack.lm::nlsLM_none ***
## minpack.lm nlsLM none i 5 summary statistics 0.0826 0.0068 0.0734 0.1839 time 0.41
## minpack.lm nlsLM none i 10 summary statistics 0.0055 0 0.0044 0.016 time 0.38
## minpack.lm nlsLM none i 15 summary statistics 0.0807 0.0065 0.0735 0.149 time 0.37
## minpack.lm nlsLM none i 20 summary statistics 0.0777 0.006 0.0687 0.1698 time 0.41
##
## ________________________________________________________________________________
## *** mFriedman_monmlp::monmlp.fit_BFGS ***
## monmlp monmlp.fit BFGS i 5 summary statistics 0.081 0.0066 0.0733 0.1642 time 0.3
## monmlp monmlp.fit BFGS i 10 summary statistics 0.0184 0.0003 0.0139 0.062 time 0.3
## monmlp monmlp.fit BFGS i 15 summary statistics 0.0141 0.0002 0.0112 0.045 time 0.3
## monmlp monmlp.fit BFGS i 20 summary statistics 0.0151 0.0002 0.012 0.0481 time 0.29
##
## ________________________________________________________________________________
## *** mFriedman_nlsr::nlxb_none ***
## 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
## nlsr nlxb none i 5 summary statistics 0.0056 0 0.0044 0.0189 time 0.67
## 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
## nlsr nlxb none i 10 summary statistics 0.0055 0 0.0043 0.0174 time 0.83
## 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
## nlsr nlxb none i 15 summary statistics 0.0055 0 0.0043 0.0183 time 0.69
## 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
## nlsr nlxb none i 20 summary statistics 0.0796 0.0063 0.0711 0.1648 time 0.86
##
## ________________________________________________________________________________
## *** mFriedman_nnet::nnet_none ***
## nnet nnet none i 5 summary statistics 0.0805 0.0065 0.0733 0.156 time 0.08
## nnet nnet none i 10 summary statistics 0.0193 0.0004 0.0155 0.0577 time 0.09
## nnet nnet none i 15 summary statistics 0.0098 0.0001 0.0078 0.0303 time 0.11
## nnet nnet none i 20 summary statistics 0.0099 0.0001 0.0077 0.0304 time 0.1
##
## ________________________________________________________________________________
## *** mFriedman_qrnn::qrnn.fit_none ***
## qrnn qrnn.fit none i 5 summary statistics 0.0922 0.0085 0.07 0.2003 time 0.44
## qrnn qrnn.fit none i 10 summary statistics 0.0878 0.0077 0.0673 0.2326 time 0.26
## qrnn qrnn.fit none i 15 summary statistics 0.1132 0.0128 0.0851 0.3845 time 0.86
## qrnn qrnn.fit none i 20 summary statistics 0.0899 0.0081 0.071 0.2284 time 0.87
##
## ________________________________________________________________________________
## *** mFriedman_radiant.model::nn_none ***
## radiant.model nn none i 5 summary statistics 0.0079 0.0001 0.0063 0.0232 time 0.14
## radiant.model nn none i 10 summary statistics 0.0142 0.0002 0.0112 0.0471 time 0.11
## radiant.model nn none i 15 summary statistics 0.0154 0.0002 0.0117 0.0543 time 0.12
## radiant.model nn none i 20 summary statistics 0.0108 0.0001 0.0084 0.0341 time 0.11
##
## ________________________________________________________________________________
## *** mFriedman_rminer::fit_none ***
## rminer fit none i 5 summary statistics 0.0106 0.0001 0.0084 0.0373 time 0.28
## rminer fit none i 10 summary statistics 0.0167 0.0003 0.0126 0.0611 time 0.3
## rminer fit none i 15 summary statistics 0.0094 0.0001 0.0074 0.0298 time 0.3
## rminer fit none i 20 summary statistics 0.0105 0.0001 0.0082 0.0319 time 0.29
##
## ________________________________________________________________________________
## *** mFriedman_validann::ann_BFGS ***
## initial value 634.656971
## iter 20 value 139.664666
## iter 40 value 94.059374
## iter 60 value 36.830455
## iter 80 value 16.541336
## iter 100 value 11.000139
## iter 120 value 4.362652
## iter 140 value 1.848193
## iter 160 value 1.393039
## iter 180 value 1.268947
## iter 200 value 0.872247
## final value 0.872247
## stopped after 200 iterations
## initial value 605.937598
## iter 20 value 161.648756
## iter 40 value 103.162182
## iter 60 value 73.792806
## iter 80 value 65.747401
## iter 100 value 58.080262
## iter 120 value 20.430317
## iter 140 value 8.681500
## iter 160 value 6.958512
## iter 180 value 5.501597
## iter 200 value 3.153897
## final value 3.153897
## stopped after 200 iterations
## initial value 438.584423
## iter 20 value 140.052967
## iter 40 value 103.431950
## iter 60 value 52.348686
## iter 80 value 27.418764
## iter 100 value 19.199605
## iter 120 value 8.858240
## iter 140 value 3.329910
## iter 160 value 2.501788
## iter 180 value 2.171515
## iter 200 value 1.450462
## final value 1.450462
## stopped after 200 iterations
## initial value 529.076064
## iter 20 value 142.767106
## iter 40 value 93.836170
## iter 60 value 66.159971
## iter 80 value 63.591520
## iter 100 value 62.198882
## iter 120 value 61.175637
## iter 140 value 60.479379
## iter 160 value 60.227473
## iter 180 value 60.038338
## iter 200 value 59.688770
## final value 59.688770
## stopped after 200 iterations
## initial value 616.947209
## iter 20 value 151.848968
## iter 40 value 120.227678
## iter 60 value 44.976500
## iter 80 value 14.701318
## iter 100 value 7.821095
## iter 120 value 6.407984
## iter 140 value 4.289362
## iter 160 value 2.807430
## iter 180 value 2.612179
## iter 200 value 2.176290
## final value 2.176290
## stopped after 200 iterations
## validann ann BFGS i 5 summary statistics 0.0155 0.0002 0.0125 0.0576 time 2.59
## initial value 464.518478
## iter 20 value 158.839087
## iter 40 value 102.966041
## iter 60 value 73.243566
## iter 80 value 68.521788
## iter 100 value 66.439941
## iter 120 value 34.904380
## iter 140 value 9.181963
## iter 160 value 8.085335
## iter 180 value 6.532667
## iter 200 value 4.629442
## final value 4.629442
## stopped after 200 iterations
## initial value 572.362350
## iter 20 value 159.625839
## iter 40 value 117.214025
## iter 60 value 59.945495
## iter 80 value 43.511892
## iter 100 value 29.809131
## iter 120 value 18.587117
## iter 140 value 12.397435
## iter 160 value 10.422113
## iter 180 value 9.023546
## iter 200 value 5.882148
## final value 5.882148
## stopped after 200 iterations
## initial value 569.921358
## iter 20 value 120.174652
## iter 40 value 65.923364
## iter 60 value 50.617804
## iter 80 value 45.693241
## iter 100 value 37.305478
## iter 120 value 11.617729
## iter 140 value 5.033441
## iter 160 value 3.874031
## iter 180 value 3.042749
## iter 200 value 1.474503
## final value 1.474503
## stopped after 200 iterations
## initial value 615.533182
## iter 20 value 138.035015
## iter 40 value 95.419492
## iter 60 value 73.782301
## iter 80 value 69.331414
## iter 100 value 66.934111
## iter 120 value 63.892231
## iter 140 value 62.302669
## iter 160 value 61.930324
## iter 180 value 61.042970
## iter 200 value 59.897921
## final value 59.897921
## stopped after 200 iterations
## initial value 637.133474
## iter 20 value 155.090126
## iter 40 value 139.907738
## iter 60 value 75.685242
## iter 80 value 70.082830
## iter 100 value 67.771347
## iter 120 value 65.169166
## iter 140 value 18.527731
## iter 160 value 14.098533
## iter 180 value 8.076604
## iter 200 value 4.284397
## final value 4.284397
## stopped after 200 iterations
## validann ann BFGS i 10 summary statistics 0.0218 0.0005 0.0168 0.0844 time 2.37
## initial value 504.744691
## iter 20 value 117.206774
## iter 40 value 57.507863
## iter 60 value 27.767674
## iter 80 value 18.929851
## iter 100 value 13.216555
## iter 120 value 7.292079
## iter 140 value 2.755930
## iter 160 value 2.086204
## iter 180 value 1.760229
## iter 200 value 1.258573
## final value 1.258573
## stopped after 200 iterations
## initial value 482.150934
## iter 20 value 147.835604
## iter 40 value 69.219140
## iter 60 value 19.170635
## iter 80 value 12.209941
## iter 100 value 7.969851
## iter 120 value 3.683457
## iter 140 value 1.557993
## iter 160 value 1.200528
## iter 180 value 1.077435
## iter 200 value 0.827011
## final value 0.827011
## stopped after 200 iterations
## initial value 674.522724
## iter 20 value 160.048912
## iter 40 value 107.297566
## iter 60 value 29.171597
## iter 80 value 18.635347
## iter 100 value 14.298503
## iter 120 value 5.223750
## iter 140 value 2.005718
## iter 160 value 1.654758
## iter 180 value 1.463163
## iter 200 value 1.015181
## final value 1.015181
## stopped after 200 iterations
## initial value 486.689772
## iter 20 value 115.004223
## iter 40 value 76.655893
## iter 60 value 64.651752
## iter 80 value 62.729036
## iter 100 value 62.027728
## iter 120 value 60.241019
## iter 140 value 58.416607
## iter 160 value 56.772664
## iter 180 value 56.405486
## iter 200 value 56.370445
## final value 56.370445
## stopped after 200 iterations
## initial value 553.478961
## iter 20 value 124.310177
## iter 40 value 50.513614
## iter 60 value 20.839390
## iter 80 value 12.688006
## iter 100 value 8.534538
## iter 120 value 4.473195
## iter 140 value 1.981601
## iter 160 value 1.516064
## iter 180 value 1.308064
## iter 200 value 0.920083
## final value 0.920083
## stopped after 200 iterations
## validann ann BFGS i 15 summary statistics 0.0101 0.0001 0.008 0.0294 time 2.33
## initial value 760.925576
## iter 20 value 104.396468
## iter 40 value 69.427978
## iter 60 value 45.536595
## iter 80 value 25.116088
## iter 100 value 13.404531
## iter 120 value 7.059737
## iter 140 value 5.296161
## iter 160 value 4.816201
## iter 180 value 4.238855
## iter 200 value 3.062763
## final value 3.062763
## stopped after 200 iterations
## initial value 401.531767
## iter 20 value 188.191106
## iter 40 value 119.506621
## iter 60 value 36.171375
## iter 80 value 15.273841
## iter 100 value 10.536490
## iter 120 value 5.009293
## iter 140 value 2.399425
## iter 160 value 1.920798
## iter 180 value 1.715715
## iter 200 value 1.036093
## final value 1.036093
## stopped after 200 iterations
## initial value 674.747995
## iter 20 value 141.958509
## iter 40 value 69.067573
## iter 60 value 42.932150
## iter 80 value 32.147231
## iter 100 value 28.766365
## iter 120 value 13.633563
## iter 140 value 6.196975
## iter 160 value 4.460022
## iter 180 value 3.706693
## iter 200 value 2.659680
## final value 2.659680
## stopped after 200 iterations
## initial value 572.260796
## iter 20 value 148.596206
## iter 40 value 74.861858
## iter 60 value 34.903985
## iter 80 value 12.992240
## iter 100 value 10.170706
## iter 120 value 7.415042
## iter 140 value 5.655385
## iter 160 value 4.600740
## iter 180 value 3.811553
## iter 200 value 2.884984
## final value 2.884984
## stopped after 200 iterations
## initial value 535.757044
## iter 20 value 151.041308
## iter 40 value 102.073222
## iter 60 value 84.646410
## iter 80 value 62.723159
## iter 100 value 51.062995
## iter 120 value 44.545152
## iter 140 value 44.022598
## iter 160 value 43.951208
## iter 180 value 43.777338
## iter 200 value 43.643419
## final value 43.643419
## stopped after 200 iterations
## validann ann BFGS i 20 summary statistics 0.0694 0.0048 0.0494 0.1649 time 2.35
##
## ________________________________________________________________________________
## *** mFriedman_validann::ann_L-BFGS-B ***
## iter 20 value 115.410359
## iter 40 value 71.735057
## iter 60 value 66.508041
## iter 80 value 63.996857
## iter 100 value 62.563961
## iter 120 value 61.965672
## iter 140 value 61.499430
## iter 160 value 61.363738
## iter 180 value 61.193179
## iter 200 value 60.933522
## final value 60.917431
## stopped after 201 iterations
## iter 20 value 112.457721
## iter 40 value 66.456642
## iter 60 value 63.975728
## iter 80 value 10.811013
## iter 100 value 6.845473
## iter 120 value 5.824719
## iter 140 value 4.843813
## iter 160 value 4.329903
## iter 180 value 3.987277
## iter 200 value 3.581026
## final value 3.565785
## stopped after 201 iterations
## iter 20 value 138.238372
## iter 40 value 27.319376
## iter 60 value 17.808617
## iter 80 value 13.503587
## iter 100 value 9.590898
## iter 120 value 8.363997
## iter 140 value 7.202265
## iter 160 value 6.014377
## iter 180 value 5.710757
## iter 200 value 5.333754
## final value 5.331037
## stopped after 201 iterations
## iter 20 value 68.913330
## iter 40 value 39.794091
## iter 60 value 27.327510
## iter 80 value 18.047588
## iter 100 value 13.045513
## iter 120 value 11.584331
## iter 140 value 10.457994
## iter 160 value 9.956336
## iter 180 value 9.348795
## iter 200 value 8.721076
## final value 8.687453
## stopped after 201 iterations
## iter 20 value 137.664729
## iter 40 value 95.139339
## iter 60 value 73.674376
## iter 80 value 69.335915
## iter 100 value 66.813962
## iter 120 value 65.894461
## iter 140 value 61.999183
## iter 160 value 13.538243
## iter 180 value 7.747209
## iter 200 value 7.210290
## final value 7.194532
## stopped after 201 iterations
## validann ann L-BFGS-B i 5 summary statistics 0.0282 0.0008 0.0227 0.1097 time 2.46
## iter 20 value 97.562725
## iter 40 value 66.050407
## iter 60 value 62.402904
## iter 80 value 60.915581
## iter 100 value 59.533891
## iter 120 value 59.044220
## iter 140 value 58.762616
## iter 160 value 58.700964
## iter 180 value 58.669636
## iter 200 value 58.594541
## final value 58.590294
## stopped after 201 iterations
## iter 20 value 107.594517
## iter 40 value 66.865907
## iter 60 value 61.936596
## iter 80 value 59.579790
## iter 100 value 59.125141
## iter 120 value 58.921068
## iter 140 value 58.718794
## iter 160 value 58.664562
## iter 180 value 58.572975
## iter 200 value 58.448598
## final value 58.444455
## stopped after 201 iterations
## iter 20 value 114.463508
## iter 40 value 70.713445
## iter 60 value 61.565170
## iter 80 value 59.283609
## iter 100 value 20.726726
## iter 120 value 8.081139
## iter 140 value 6.063830
## iter 160 value 5.445479
## iter 180 value 4.970659
## iter 200 value 4.209067
## final value 4.161692
## stopped after 201 iterations
## iter 20 value 118.977056
## iter 40 value 73.841191
## iter 60 value 65.103811
## iter 80 value 63.179118
## iter 100 value 62.497964
## iter 120 value 62.128945
## iter 140 value 61.735894
## iter 160 value 61.239722
## iter 180 value 61.001749
## iter 200 value 60.817576
## final value 60.815498
## stopped after 201 iterations
## iter 20 value 76.493496
## iter 40 value 66.310833
## iter 60 value 64.579080
## iter 80 value 62.679376
## iter 100 value 60.608786
## iter 120 value 59.460868
## iter 140 value 59.201781
## iter 160 value 58.954376
## iter 180 value 58.424733
## iter 200 value 58.307401
## final value 58.305287
## stopped after 201 iterations
## validann ann L-BFGS-B i 10 summary statistics 0.0803 0.0064 0.0732 0.148 time 2.44
## iter 20 value 96.283300
## iter 40 value 49.447441
## iter 60 value 44.515828
## iter 80 value 43.277881
## iter 100 value 43.160954
## iter 120 value 43.115599
## iter 140 value 43.098381
## iter 160 value 43.088740
## iter 180 value 43.082286
## iter 200 value 43.065038
## final value 43.064254
## stopped after 201 iterations
## iter 20 value 106.578467
## iter 40 value 27.580635
## iter 60 value 16.997454
## iter 80 value 12.432325
## iter 100 value 9.915960
## iter 120 value 8.910474
## iter 140 value 7.685042
## iter 160 value 6.392227
## iter 180 value 5.692784
## iter 200 value 5.467264
## final value 5.458076
## stopped after 201 iterations
## iter 20 value 131.915650
## iter 40 value 49.191638
## iter 60 value 44.916327
## iter 80 value 43.237775
## iter 100 value 43.123639
## iter 120 value 43.104938
## iter 140 value 43.077966
## iter 160 value 43.060086
## iter 180 value 43.052323
## iter 200 value 43.038163
## final value 43.037159
## stopped after 201 iterations
## iter 20 value 134.393120
## iter 40 value 68.488541
## iter 60 value 63.393124
## iter 80 value 58.200034
## iter 100 value 13.923327
## iter 120 value 9.023222
## iter 140 value 7.364023
## iter 160 value 5.410680
## iter 180 value 4.585074
## iter 200 value 4.374318
## final value 4.368860
## stopped after 201 iterations
## iter 20 value 77.869146
## iter 40 value 66.473488
## iter 60 value 24.006001
## iter 80 value 8.161988
## iter 100 value 5.913608
## iter 120 value 5.281141
## iter 140 value 4.865318
## iter 160 value 4.365936
## iter 180 value 3.888420
## iter 200 value 3.383310
## final value 3.341624
## stopped after 201 iterations
## validann ann L-BFGS-B i 15 summary statistics 0.0192 0.0004 0.0151 0.0603 time 2.39
## iter 20 value 119.509204
## iter 40 value 69.490615
## iter 60 value 66.115618
## iter 80 value 19.956786
## iter 100 value 15.093888
## iter 120 value 12.639585
## iter 140 value 9.742284
## iter 160 value 7.315019
## iter 180 value 6.375203
## iter 200 value 5.792492
## final value 5.780983
## stopped after 201 iterations
## iter 20 value 133.636359
## iter 40 value 76.521256
## iter 60 value 24.207387
## iter 80 value 11.993910
## iter 100 value 7.266124
## iter 120 value 5.532262
## iter 140 value 4.662019
## iter 160 value 4.315583
## iter 180 value 4.014430
## iter 200 value 3.634452
## final value 3.615488
## stopped after 201 iterations
## iter 20 value 137.383336
## iter 40 value 74.618807
## iter 60 value 66.213932
## iter 80 value 64.285240
## iter 100 value 63.278314
## iter 120 value 61.816792
## iter 140 value 61.437825
## iter 160 value 61.206600
## iter 180 value 60.839128
## iter 200 value 60.715268
## final value 60.713030
## stopped after 201 iterations
## iter 20 value 121.465278
## iter 40 value 79.956846
## iter 60 value 65.556308
## iter 80 value 62.867260
## iter 100 value 17.413396
## iter 120 value 9.774182
## iter 140 value 7.948130
## iter 160 value 6.408346
## iter 180 value 4.919358
## iter 200 value 4.221615
## final value 4.201241
## stopped after 201 iterations
## iter 20 value 82.017283
## iter 40 value 47.226383
## iter 60 value 12.010103
## iter 80 value 8.582352
## iter 100 value 6.133623
## iter 120 value 5.129900
## iter 140 value 3.780616
## iter 160 value 3.303327
## iter 180 value 2.861724
## iter 200 value 2.616931
## final value 2.611372
## stopped after 201 iterations
## validann ann L-BFGS-B i 20 summary statistics 0.017 0.0003 0.0135 0.0557 time 2.38
##
## ________________________________________________________________________________
## *** mIshigami_brnn::brnn_gaussNewton ***
## Number of parameters (weights and biases) to estimate: 50
## Nguyen-Widrow method
## Scaling factor= 0.7032311
## gamma= 44.381 alpha= 0.0982 beta= 14.1204
## Number of parameters (weights and biases) to estimate: 50
## Nguyen-Widrow method
## Scaling factor= 0.7032311
## gamma= 44.3824 alpha= 0.0982 beta= 14.1205
## Number of parameters (weights and biases) to estimate: 50
## Nguyen-Widrow method
## Scaling factor= 0.7032311
## gamma= 43.7171 alpha= 0.1002 beta= 13.7586
## Number of parameters (weights and biases) to estimate: 50
## Nguyen-Widrow method
## Scaling factor= 0.7032311
## gamma= 44.3824 alpha= 0.0982 beta= 14.1204
## Number of parameters (weights and biases) to estimate: 50
## Nguyen-Widrow method
## Scaling factor= 0.7032311
## gamma= 25.3797 alpha= 0.356 beta= 0.9698
## brnn brnn gaussNewton i 5 summary statistics 2.5838 6.6761 2.2419 6.5182 time 0.3
## Number of parameters (weights and biases) to estimate: 50
## Nguyen-Widrow method
## Scaling factor= 0.7032311
## gamma= 44.6998 alpha= 0.112 beta= 13.5765
## Number of parameters (weights and biases) to estimate: 50
## Nguyen-Widrow method
## Scaling factor= 0.7032311
## gamma= 44.3791 alpha= 0.0981 beta= 14.1211
## Number of parameters (weights and biases) to estimate: 50
## Nguyen-Widrow method
## Scaling factor= 0.7032311
## gamma= 24.2228 alpha= 0.3731 beta= 0.9663
## Number of parameters (weights and biases) to estimate: 50
## Nguyen-Widrow method
## Scaling factor= 0.7032311
## gamma= 45.0405 alpha= 0.0908 beta= 14.0583
## Number of parameters (weights and biases) to estimate: 50
## Nguyen-Widrow method
## Scaling factor= 0.7032311
## gamma= 45.0399 alpha= 0.0909 beta= 14.0578
## brnn brnn gaussNewton i 10 summary statistics 0.6645 0.4415 0.5062 2.9204 time 0.26
## Number of parameters (weights and biases) to estimate: 50
## Nguyen-Widrow method
## Scaling factor= 0.7032311
## gamma= 44.8414 alpha= 0.0786 beta= 14.1468
## Number of parameters (weights and biases) to estimate: 50
## Nguyen-Widrow method
## Scaling factor= 0.7032311
## gamma= 24.2191 alpha= 0.3736 beta= 0.9662
## Number of parameters (weights and biases) to estimate: 50
## Nguyen-Widrow method
## Scaling factor= 0.7032311
## gamma= 45.9756 alpha= 0.0876 beta= 13.8744
## Number of parameters (weights and biases) to estimate: 50
## Nguyen-Widrow method
## Scaling factor= 0.7032311
## gamma= 44.3814 alpha= 0.0981 beta= 14.1215
## Number of parameters (weights and biases) to estimate: 50
## Nguyen-Widrow method
## Scaling factor= 0.7032311
## gamma= 26.5992 alpha= 0.4413 beta= 0.9577
## brnn brnn gaussNewton i 15 summary statistics 2.5968 6.7436 2.2495 6.6903 time 0.17
## Number of parameters (weights and biases) to estimate: 50
## Nguyen-Widrow method
## Scaling factor= 0.7032311
## gamma= 46.6231 alpha= 0.0267 beta= 14.318
## Number of parameters (weights and biases) to estimate: 50
## Nguyen-Widrow method
## Scaling factor= 0.7032311
## gamma= 46.7868 alpha= 0.0861 beta= 15.2747
## Number of parameters (weights and biases) to estimate: 50
## Nguyen-Widrow method
## Scaling factor= 0.7032311
## gamma= 24.2569 alpha= 0.3678 beta= 0.967
## Number of parameters (weights and biases) to estimate: 50
## Nguyen-Widrow method
## Scaling factor= 0.7032311
## gamma= 43.7381 alpha= 0.1025 beta= 13.7212
## Number of parameters (weights and biases) to estimate: 50
## Nguyen-Widrow method
## Scaling factor= 0.7032311
## gamma= 44.8387 alpha= 0.0786 beta= 14.1464
## brnn brnn gaussNewton i 20 summary statistics 0.6625 0.4389 0.51 2.9355 time 0.22
##
## ________________________________________________________________________________
## *** mIshigami_CaDENCE::cadence.fit_optim ***
## n.hidden = 10 --> 1 * NLL = 205.6286 ; penalty = 0; BIC = 796.5629 ; AICc = 553.1337 ; AIC = 535.2572
## n.hidden = 10 --> 1 * NLL = 398.2521 ; penalty = 0; BIC = 1181.81 ; AICc = 938.3806 ; AIC = 920.5042
## n.hidden = 10 --> 1 * NLL = -134.9285 ; penalty = 0; BIC = 115.4486 ; AICc = -127.9806 ; AIC = -145.8571
## n.hidden = 10 --> 1 * NLL = 146.3407 ; penalty = 0; BIC = 677.987 ; AICc = 434.5577 ; AIC = 416.6813
## n.hidden = 10 --> 1 * NLL = 272.5537 ; penalty = 0; BIC = 930.413 ; AICc = 686.9838 ; AIC = 669.1073
## CaDENCE cadence.fit optim i 5 summary statistics 2.0786 4.3207 1.578 6.0159 time 16.47
## n.hidden = 10 --> 1 * NLL = 505.0193 ; penalty = 0; BIC = 1395.344 ; AICc = 1151.915 ; AIC = 1134.039
## n.hidden = 10 --> 1 * NLL = 339.4834 ; penalty = 0; BIC = 1064.273 ; AICc = 820.8433 ; AIC = 802.9669
## n.hidden = 10 --> 1 * NLL = 12.15625 ; penalty = 0; BIC = 409.6182 ; AICc = 166.1889 ; AIC = 148.3125
## n.hidden = 10 --> 1 * NLL = 169.655 ; penalty = 0; BIC = 724.6157 ; AICc = 481.1865 ; AIC = 463.31
## n.hidden = 10 --> 1 * NLL = 394.2059 ; penalty = 0; BIC = 1173.718 ; AICc = 930.2883 ; AIC = 912.4119
## CaDENCE cadence.fit optim i 10 summary statistics 2.4104 5.8099 1.8811 8.4503 time 16.28
## n.hidden = 10 --> 1 * NLL = 442.8615 ; penalty = 0; BIC = 1271.029 ; AICc = 1027.599 ; AIC = 1009.723
## n.hidden = 10 --> 1 * NLL = -114.1472 ; penalty = 0; BIC = 157.0113 ; AICc = -86.41801 ; AIC = -104.2944
## n.hidden = 10 --> 1 * NLL = 282.7519 ; penalty = 0; BIC = 950.8096 ; AICc = 707.3803 ; AIC = 689.5039
## n.hidden = 10 --> 1 * NLL = 493.0238 ; penalty = 0; BIC = 1371.353 ; AICc = 1127.924 ; AIC = 1110.048
## n.hidden = 10 --> 1 * NLL = 310.909 ; penalty = 0; BIC = 1007.124 ; AICc = 763.6943 ; AIC = 745.8179
## CaDENCE cadence.fit optim i 15 summary statistics 2.2443 5.0367 1.6771 7.0231 time 16.59
## n.hidden = 10 --> 1 * NLL = -113.5736 ; penalty = 0; BIC = 158.1584 ; AICc = -85.27082 ; AIC = -103.1473
## n.hidden = 10 --> 1 * NLL = 20.26683 ; penalty = 0; BIC = 425.8394 ; AICc = 182.4101 ; AIC = 164.5337
## n.hidden = 10 --> 1 * NLL = 284.7339 ; penalty = 0; BIC = 954.7736 ; AICc = 711.3443 ; AIC = 693.4679
## n.hidden = 10 --> 1 * NLL = -438.4919 ; penalty = 0; BIC = -491.678 ; AICc = -735.1073 ; AIC = -752.9837
## n.hidden = 10 --> 1 * NLL = -427.1725 ; penalty = 0; BIC = -469.0392 ; AICc = -712.4685 ; AIC = -730.3449
## CaDENCE cadence.fit optim i 20 summary statistics 1.4162 2.0055 0.7108 6.6061 time 16.54
##
## ________________________________________________________________________________
## *** mIshigami_MachineShop::fit_none ***
## MachineShop fit none i 5 summary statistics 2.2811 5.2036 1.8599 6.05 time 0.17
## MachineShop fit none i 10 summary statistics 2.2807 5.2017 1.8892 6.2706 time 0.15
## MachineShop fit none i 15 summary statistics 0.8057 0.6491 0.6206 2.6764 time 0.17
## MachineShop fit none i 20 summary statistics 0.6624 0.4388 0.5126 2.7589 time 0.16
##
## ________________________________________________________________________________
## *** mIshigami_minpack.lm::nlsLM_none ***
## minpack.lm nlsLM none i 5 summary statistics 1.9386 3.7581 1.4874 5.604 time 0.97
## minpack.lm nlsLM none i 10 summary statistics 0.6116 0.374 0.4759 2.4629 time 0.94
## minpack.lm nlsLM none i 15 summary statistics 0.6972 0.4861 0.5167 2.9456 time 0.93
## minpack.lm nlsLM none i 20 summary statistics 2.486 6.18 2.1724 5.5562 time 0.94
##
## ________________________________________________________________________________
## *** mIshigami_monmlp::monmlp.fit_BFGS ***
## monmlp monmlp.fit BFGS i 5 summary statistics 0.8035 0.6456 0.5941 3.5394 time 0.45
## monmlp monmlp.fit BFGS i 10 summary statistics 0.7115 0.5062 0.5279 3.0381 time 0.47
## monmlp monmlp.fit BFGS i 15 summary statistics 0.7213 0.5202 0.5509 3.0227 time 0.5
## monmlp monmlp.fit BFGS i 20 summary statistics 0.7365 0.5425 0.558 2.9218 time 0.48
##
## ________________________________________________________________________________
## *** mIshigami_nlsr::nlxb_none ***
## 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
## nlsr nlxb none i 5 summary statistics 2.2897 5.2428 1.8725 6.048 time 1.34
## 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
## nlsr nlxb none i 10 summary statistics 2.3789 5.6593 2.0821 5.7604 time 1.25
## 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
## nlsr nlxb none i 15 summary statistics 2.4541 6.0228 2.129 5.6488 time 1.41
## 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
## nlsr nlxb none i 20 summary statistics 2.3833 5.6801 2.0408 5.7826 time 1.39
##
## ________________________________________________________________________________
## *** mIshigami_nnet::nnet_none ***
## nnet nnet none i 5 summary statistics 0.6641 0.441 0.501 2.9019 time 0.15
## nnet nnet none i 10 summary statistics 0.5844 0.3415 0.4174 3.048 time 0.16
## nnet nnet none i 15 summary statistics 0.7168 0.5138 0.534 2.8974 time 0.14
## nnet nnet none i 20 summary statistics 0.6494 0.4217 0.4791 3.2118 time 0.16
##
## ________________________________________________________________________________
## *** mIshigami_qrnn::qrnn.fit_none ***
## qrnn qrnn.fit none i 5 summary statistics 2.2972 5.277 1.7384 7.4505 time 1.03
## qrnn qrnn.fit none i 10 summary statistics 0.7379 0.5445 0.4662 3.6498 time 0.98
## qrnn qrnn.fit none i 15 summary statistics 0.7893 0.623 0.484 4.0107 time 1.13
## qrnn qrnn.fit none i 20 summary statistics 0.806 0.6496 0.53 3.8835 time 1.17
##
## ________________________________________________________________________________
## *** mIshigami_radiant.model::nn_none ***
## radiant.model nn none i 5 summary statistics 0.696 0.4843 0.4999 3.394 time 0.19
## radiant.model nn none i 10 summary statistics 0.6666 0.4444 0.5059 2.4906 time 0.19
## radiant.model nn none i 15 summary statistics 0.676 0.457 0.5162 2.6674 time 0.17
## radiant.model nn none i 20 summary statistics 0.6678 0.446 0.5066 2.7355 time 0.21
##
## ________________________________________________________________________________
## *** mIshigami_rminer::fit_none ***
## rminer fit none i 5 summary statistics 0.6841 0.468 0.5121 2.8359 time 0.47
## rminer fit none i 10 summary statistics 0.651 0.4238 0.4516 3.2744 time 0.46
## rminer fit none i 15 summary statistics 0.652 0.4251 0.4953 3.1128 time 0.43
## rminer fit none i 20 summary statistics 0.6535 0.4271 0.499 2.8501 time 0.46
##
## ________________________________________________________________________________
## *** mIshigami_validann::ann_BFGS ***
## initial value 456.022092
## iter 20 value 346.704145
## iter 40 value 275.490937
## iter 60 value 179.853993
## iter 80 value 129.317755
## iter 100 value 118.047965
## iter 120 value 112.326345
## iter 140 value 106.396728
## iter 160 value 70.622884
## iter 180 value 28.010553
## iter 200 value 18.603934
## final value 18.603934
## stopped after 200 iterations
## initial value 568.949622
## iter 20 value 307.369733
## iter 40 value 268.748263
## iter 60 value 249.160426
## iter 80 value 238.187939
## iter 100 value 231.185925
## iter 120 value 222.611742
## iter 140 value 217.979960
## iter 160 value 215.157046
## iter 180 value 212.783317
## iter 200 value 211.509886
## final value 211.509886
## stopped after 200 iterations
## initial value 948.143900
## iter 20 value 298.630459
## iter 40 value 246.466323
## iter 60 value 206.501981
## iter 80 value 198.733484
## iter 100 value 195.165717
## iter 120 value 194.396958
## iter 140 value 193.065385
## iter 160 value 190.768723
## iter 180 value 189.051669
## iter 200 value 187.635317
## final value 187.635317
## stopped after 200 iterations
## initial value 486.819908
## iter 20 value 318.988411
## iter 40 value 228.486764
## iter 60 value 127.682964
## iter 80 value 54.159191
## iter 100 value 33.360353
## iter 120 value 29.818935
## iter 140 value 26.892898
## iter 160 value 22.750856
## iter 180 value 20.515284
## iter 200 value 19.286952
## final value 19.286952
## stopped after 200 iterations
## initial value 1199.505985
## iter 20 value 351.691776
## iter 40 value 269.606936
## iter 60 value 154.938058
## iter 80 value 99.841282
## iter 100 value 48.703187
## iter 120 value 39.881225
## iter 140 value 32.956251
## iter 160 value 25.720497
## iter 180 value 22.820843
## iter 200 value 20.835354
## final value 20.835354
## stopped after 200 iterations
## validann ann BFGS i 5 summary statistics 0.754 0.5685 0.5443 3.2284 time 4.71
## initial value 622.931526
## iter 20 value 288.455328
## iter 40 value 250.108968
## iter 60 value 169.074093
## iter 80 value 139.178461
## iter 100 value 87.042335
## iter 120 value 29.030764
## iter 140 value 21.942326
## iter 160 value 19.201385
## iter 180 value 17.469483
## iter 200 value 16.960091
## final value 16.960091
## stopped after 200 iterations
## initial value 525.757838
## iter 20 value 263.654135
## iter 40 value 198.030494
## iter 60 value 143.819973
## iter 80 value 106.226386
## iter 100 value 95.701383
## iter 120 value 91.948735
## iter 140 value 65.339872
## iter 160 value 26.238238
## iter 180 value 20.076186
## iter 200 value 18.339275
## final value 18.339275
## stopped after 200 iterations
## initial value 687.219061
## iter 20 value 273.422206
## iter 40 value 238.612001
## iter 60 value 158.250445
## iter 80 value 31.740445
## iter 100 value 25.103525
## iter 120 value 24.055508
## iter 140 value 21.856722
## iter 160 value 19.828590
## iter 180 value 19.002974
## iter 200 value 18.687965
## final value 18.687965
## stopped after 200 iterations
## initial value 523.641085
## iter 20 value 313.164101
## iter 40 value 247.047650
## iter 60 value 110.932474
## iter 80 value 52.646900
## iter 100 value 30.979924
## iter 120 value 25.433884
## iter 140 value 21.822862
## iter 160 value 18.583852
## iter 180 value 16.737516
## iter 200 value 16.055078
## final value 16.055078
## stopped after 200 iterations
## initial value 777.435618
## iter 20 value 310.188974
## iter 40 value 243.548985
## iter 60 value 164.922997
## iter 80 value 82.390193
## iter 100 value 27.960109
## iter 120 value 24.309413
## iter 140 value 21.714015
## iter 160 value 18.621801
## iter 180 value 17.012485
## iter 200 value 15.808271
## final value 15.808271
## stopped after 200 iterations
## validann ann BFGS i 10 summary statistics 0.6567 0.4313 0.4974 2.8511 time 5.05
## initial value 1000.347483
## iter 20 value 285.869287
## iter 40 value 198.384362
## iter 60 value 144.077769
## iter 80 value 125.568130
## iter 100 value 117.890095
## iter 120 value 112.501095
## iter 140 value 97.802220
## iter 160 value 28.849061
## iter 180 value 20.024758
## iter 200 value 18.266423
## final value 18.266423
## stopped after 200 iterations
## initial value 560.131062
## iter 20 value 272.215956
## iter 40 value 232.781128
## iter 60 value 208.984087
## iter 80 value 201.256395
## iter 100 value 197.670970
## iter 120 value 196.967012
## iter 140 value 195.433650
## iter 160 value 189.099600
## iter 180 value 172.974413
## iter 200 value 165.254123
## final value 165.254123
## stopped after 200 iterations
## initial value 749.195386
## iter 20 value 286.104769
## iter 40 value 229.331468
## iter 60 value 170.249259
## iter 80 value 106.342114
## iter 100 value 86.926174
## iter 120 value 53.485801
## iter 140 value 26.003561
## iter 160 value 21.439835
## iter 180 value 20.821739
## iter 200 value 20.234818
## final value 20.234818
## stopped after 200 iterations
## initial value 690.934386
## iter 20 value 286.882475
## iter 40 value 230.476523
## iter 60 value 206.735679
## iter 80 value 174.612207
## iter 100 value 151.240084
## iter 120 value 150.424535
## iter 140 value 148.346005
## iter 160 value 93.006629
## iter 180 value 62.390323
## iter 200 value 30.623261
## final value 30.623261
## stopped after 200 iterations
## initial value 696.135663
## iter 20 value 283.109539
## iter 40 value 238.849413
## iter 60 value 194.596999
## iter 80 value 152.917445
## iter 100 value 141.074686
## iter 120 value 118.471441
## iter 140 value 99.253249
## iter 160 value 65.264067
## iter 180 value 35.052898
## iter 200 value 28.965777
## final value 28.965777
## stopped after 200 iterations
## validann ann BFGS i 15 summary statistics 0.889 0.7903 0.6814 3.9129 time 4.83
## initial value 526.875358
## iter 20 value 283.884093
## iter 40 value 238.087645
## iter 60 value 186.369214
## iter 80 value 107.043002
## iter 100 value 41.135832
## iter 120 value 34.420001
## iter 140 value 29.841280
## iter 160 value 23.402687
## iter 180 value 21.218022
## iter 200 value 20.873418
## final value 20.873418
## stopped after 200 iterations
## initial value 650.759404
## iter 20 value 308.846287
## iter 40 value 266.900868
## iter 60 value 223.318765
## iter 80 value 212.026985
## iter 100 value 209.867196
## iter 120 value 207.909564
## iter 140 value 205.529652
## iter 160 value 200.099683
## iter 180 value 194.421960
## iter 200 value 191.057789
## final value 191.057789
## stopped after 200 iterations
## initial value 723.163834
## iter 20 value 280.891634
## iter 40 value 257.058683
## iter 60 value 241.938802
## iter 80 value 234.977204
## iter 100 value 203.623668
## iter 120 value 199.531175
## iter 140 value 192.390840
## iter 160 value 187.035164
## iter 180 value 185.177847
## iter 200 value 183.041195
## final value 183.041195
## stopped after 200 iterations
## initial value 600.785244
## iter 20 value 288.206600
## iter 40 value 259.158209
## iter 60 value 247.330967
## iter 80 value 234.053433
## iter 100 value 227.560897
## iter 120 value 226.214176
## iter 140 value 222.960769
## iter 160 value 220.234770
## iter 180 value 218.120981
## iter 200 value 216.214087
## final value 216.214087
## stopped after 200 iterations
## initial value 627.720254
## iter 20 value 264.088778
## iter 40 value 231.818477
## iter 60 value 212.701726
## iter 80 value 201.060389
## iter 100 value 199.013330
## iter 120 value 198.479679
## iter 140 value 197.321975
## iter 160 value 195.170130
## iter 180 value 194.269827
## iter 200 value 192.568519
## final value 192.568519
## stopped after 200 iterations
## validann ann BFGS i 20 summary statistics 2.2922 5.2541 1.8808 6.528 time 4.91
##
## ________________________________________________________________________________
## *** mIshigami_validann::ann_L-BFGS-B ***
## iter 20 value 291.566632
## iter 40 value 254.537677
## iter 60 value 242.380333
## iter 80 value 236.402387
## iter 100 value 205.733659
## iter 120 value 181.704234
## iter 140 value 161.428852
## iter 160 value 141.676625
## iter 180 value 129.059822
## iter 200 value 86.043275
## final value 78.194745
## stopped after 201 iterations
## iter 20 value 296.272898
## iter 40 value 250.002240
## iter 60 value 221.442465
## iter 80 value 211.405229
## iter 100 value 177.861906
## iter 120 value 147.043187
## iter 140 value 122.624254
## iter 160 value 105.066240
## iter 180 value 97.841678
## iter 200 value 92.392526
## final value 92.026876
## stopped after 201 iterations
## iter 20 value 303.122424
## iter 40 value 266.453865
## iter 60 value 247.706396
## iter 80 value 240.479575
## iter 100 value 223.473203
## iter 120 value 213.034055
## iter 140 value 211.033868
## iter 160 value 208.958123
## iter 180 value 207.322986
## iter 200 value 202.401581
## final value 201.787734
## stopped after 201 iterations
## iter 20 value 290.386400
## iter 40 value 257.975484
## iter 60 value 248.225994
## iter 80 value 229.616757
## iter 100 value 217.609020
## iter 120 value 214.724914
## iter 140 value 213.728742
## iter 160 value 212.253825
## iter 180 value 206.900011
## iter 200 value 184.638366
## final value 183.504935
## stopped after 201 iterations
## iter 20 value 282.393030
## iter 40 value 253.707242
## iter 60 value 238.527016
## iter 80 value 137.734099
## iter 100 value 76.655443
## iter 120 value 58.827235
## iter 140 value 47.602427
## iter 160 value 37.750934
## iter 180 value 32.086257
## iter 200 value 29.780606
## final value 29.702436
## stopped after 201 iterations
## validann ann L-BFGS-B i 5 summary statistics 0.9002 0.8104 0.6498 3.5796 time 5.39
## iter 20 value 281.862378
## iter 40 value 249.819042
## iter 60 value 239.677542
## iter 80 value 204.094464
## iter 100 value 63.567862
## iter 120 value 43.101147
## iter 140 value 33.119171
## iter 160 value 28.876545
## iter 180 value 26.040067
## iter 200 value 23.529358
## final value 23.402347
## stopped after 201 iterations
## iter 20 value 319.499112
## iter 40 value 247.650345
## iter 60 value 222.398913
## iter 80 value 208.261726
## iter 100 value 160.738665
## iter 120 value 115.906120
## iter 140 value 57.954704
## iter 160 value 23.597114
## iter 180 value 21.755946
## iter 200 value 21.254662
## final value 21.239165
## stopped after 201 iterations
## iter 20 value 276.630059
## iter 40 value 252.975055
## iter 60 value 244.321464
## iter 80 value 224.070666
## iter 100 value 212.951384
## iter 120 value 209.801771
## iter 140 value 208.343149
## iter 160 value 206.929840
## iter 180 value 205.571597
## iter 200 value 205.099473
## final value 205.064711
## stopped after 201 iterations
## iter 20 value 285.870510
## iter 40 value 250.224034
## iter 60 value 239.091316
## iter 80 value 223.131295
## iter 100 value 208.999209
## iter 120 value 205.469296
## iter 140 value 204.419303
## iter 160 value 201.693152
## iter 180 value 197.946344
## iter 200 value 196.767220
## final value 196.738022
## stopped after 201 iterations
## iter 20 value 303.365515
## iter 40 value 253.392907
## iter 60 value 225.271162
## iter 80 value 212.829827
## iter 100 value 207.927784
## iter 120 value 94.045481
## iter 140 value 28.567761
## iter 160 value 22.929581
## iter 180 value 20.367694
## iter 200 value 19.709723
## final value 19.688314
## stopped after 201 iterations
## validann ann L-BFGS-B i 10 summary statistics 0.7329 0.5372 0.5426 2.8209 time 5.03
## iter 20 value 294.240150
## iter 40 value 251.390499
## iter 60 value 226.040500
## iter 80 value 211.203455
## iter 100 value 131.495233
## iter 120 value 57.575164
## iter 140 value 38.462142
## iter 160 value 32.964430
## iter 180 value 28.634584
## iter 200 value 27.646348
## final value 27.606968
## stopped after 201 iterations
## iter 20 value 275.680017
## iter 40 value 247.499115
## iter 60 value 221.915520
## iter 80 value 214.570582
## iter 100 value 209.761109
## iter 120 value 206.407531
## iter 140 value 202.181505
## iter 160 value 196.234674
## iter 180 value 163.317859
## iter 200 value 156.798679
## final value 156.649094
## stopped after 201 iterations
## iter 20 value 258.922595
## iter 40 value 244.130307
## iter 60 value 235.738770
## iter 80 value 197.677353
## iter 100 value 154.839868
## iter 120 value 138.564118
## iter 140 value 113.068684
## iter 160 value 94.679702
## iter 180 value 33.437691
## iter 200 value 27.250738
## final value 27.114400
## stopped after 201 iterations
## iter 20 value 277.040173
## iter 40 value 250.511251
## iter 60 value 239.370766
## iter 80 value 220.253176
## iter 100 value 185.027471
## iter 120 value 70.727501
## iter 140 value 44.919680
## iter 160 value 34.309827
## iter 180 value 26.849001
## iter 200 value 23.384558
## final value 23.342644
## stopped after 201 iterations
## iter 20 value 283.260627
## iter 40 value 252.774430
## iter 60 value 239.803095
## iter 80 value 217.971497
## iter 100 value 189.287146
## iter 120 value 155.958716
## iter 140 value 136.025142
## iter 160 value 127.723419
## iter 180 value 96.150361
## iter 200 value 43.882052
## final value 43.288043
## stopped after 201 iterations
## validann ann L-BFGS-B i 15 summary statistics 1.0868 1.1811 0.8574 3.6047 time 5.08
## iter 20 value 271.640077
## iter 40 value 243.227195
## iter 60 value 236.597767
## iter 80 value 212.146468
## iter 100 value 173.154041
## iter 120 value 150.036398
## iter 140 value 137.755010
## iter 160 value 133.638626
## iter 180 value 130.692148
## iter 200 value 124.949177
## final value 124.704723
## stopped after 201 iterations
## iter 20 value 296.285641
## iter 40 value 254.994560
## iter 60 value 238.485623
## iter 80 value 216.124322
## iter 100 value 211.503993
## iter 120 value 208.980220
## iter 140 value 207.244410
## iter 160 value 203.801717
## iter 180 value 201.707828
## iter 200 value 195.264014
## final value 192.453300
## stopped after 201 iterations
## iter 20 value 271.139236
## iter 40 value 252.097479
## iter 60 value 237.382676
## iter 80 value 214.276074
## iter 100 value 210.878462
## iter 120 value 208.506489
## iter 140 value 194.231691
## iter 160 value 161.856646
## iter 180 value 150.280700
## iter 200 value 94.994853
## final value 92.366777
## stopped after 201 iterations
## iter 20 value 294.041568
## iter 40 value 252.230961
## iter 60 value 225.271023
## iter 80 value 206.851608
## iter 100 value 161.724680
## iter 120 value 149.466056
## iter 140 value 97.589191
## iter 160 value 35.496710
## iter 180 value 23.829489
## iter 200 value 21.591077
## final value 21.534568
## stopped after 201 iterations
## iter 20 value 284.600020
## iter 40 value 255.789659
## iter 60 value 241.945010
## iter 80 value 218.821713
## iter 100 value 215.565768
## iter 120 value 213.181499
## iter 140 value 199.108158
## iter 160 value 81.054094
## iter 180 value 60.927145
## iter 200 value 47.048349
## final value 46.218711
## stopped after 201 iterations
## validann ann L-BFGS-B i 20 summary statistics 1.123 1.261 0.8724 4.4896 time 5.17
##
## ________________________________________________________________________________
## *** mRef153_brnn::brnn_gaussNewton ***
## Number of parameters (weights and biases) to estimate: 21
## Nguyen-Widrow method
## Scaling factor= 0.7050444
## gamma= 19.1981 alpha= 0.9956 beta= 28.3084
## Number of parameters (weights and biases) to estimate: 21
## Nguyen-Widrow method
## Scaling factor= 0.7050444
## gamma= 19.1944 alpha= 0.9974 beta= 28.3013
## Number of parameters (weights and biases) to estimate: 21
## Nguyen-Widrow method
## Scaling factor= 0.7050444
## gamma= 19.1891 alpha= 1.0007 beta= 28.2879
## Number of parameters (weights and biases) to estimate: 21
## Nguyen-Widrow method
## Scaling factor= 0.7050444
## gamma= 19.3296 alpha= 0.9159 beta= 30.6295
## Number of parameters (weights and biases) to estimate: 21
## Nguyen-Widrow method
## Scaling factor= 0.7050444
## gamma= 18.7036 alpha= 1.3276 beta= 26.4415
## brnn brnn gaussNewton i 5 summary statistics 3.606 13.0035 2.6909 14.5944 time 0.03
## Number of parameters (weights and biases) to estimate: 21
## Nguyen-Widrow method
## Scaling factor= 0.7050444
## gamma= 18.6993 alpha= 1.3278 beta= 26.441
## Number of parameters (weights and biases) to estimate: 21
## Nguyen-Widrow method
## Scaling factor= 0.7050444
## gamma= 19.3289 alpha= 0.9161 beta= 30.6282
## Number of parameters (weights and biases) to estimate: 21
## Nguyen-Widrow method
## Scaling factor= 0.7050444
## gamma= 18.7008 alpha= 1.3278 beta= 26.441
## Number of parameters (weights and biases) to estimate: 21
## Nguyen-Widrow method
## Scaling factor= 0.7050444
## gamma= 19.192 alpha= 0.9985 beta= 28.2964
## Number of parameters (weights and biases) to estimate: 21
## Nguyen-Widrow method
## Scaling factor= 0.7050444
## gamma= 18.7043 alpha= 1.3277 beta= 26.4412
## brnn brnn gaussNewton i 10 summary statistics 3.606 13.0035 2.6912 14.5931 time 0.02
## Number of parameters (weights and biases) to estimate: 21
## Nguyen-Widrow method
## Scaling factor= 0.7050444
## gamma= 19.3288 alpha= 0.9162 beta= 30.6278
## Number of parameters (weights and biases) to estimate: 21
## Nguyen-Widrow method
## Scaling factor= 0.7050444
## gamma= 18.9018 alpha= 0.9586 beta= 29.2732
## Number of parameters (weights and biases) to estimate: 21
## Nguyen-Widrow method
## Scaling factor= 0.7050444
## gamma= 18.903 alpha= 0.9587 beta= 29.2724
## Number of parameters (weights and biases) to estimate: 21
## Nguyen-Widrow method
## Scaling factor= 0.7050444
## gamma= 19.3307 alpha= 0.9155 beta= 30.6314
## Number of parameters (weights and biases) to estimate: 21
## Nguyen-Widrow method
## Scaling factor= 0.7050444
## gamma= 19.1934 alpha= 0.9978 beta= 28.2994
## brnn brnn gaussNewton i 15 summary statistics 3.4793 12.1054 2.5064 13.7372 time 0.02
## Number of parameters (weights and biases) to estimate: 21
## Nguyen-Widrow method
## Scaling factor= 0.7050444
## gamma= 19.1926 alpha= 0.9983 beta= 28.2975
## Number of parameters (weights and biases) to estimate: 21
## Nguyen-Widrow method
## Scaling factor= 0.7050444
## gamma= 19.3908 alpha= 0.8705 beta= 29.1605
## 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= 19.1938 alpha= 0.9974 beta= 28.301
## Number of parameters (weights and biases) to estimate: 21
## Nguyen-Widrow method
## Scaling factor= 0.7050444
## gamma= 19.3304 alpha= 0.9155 beta= 30.6314
## brnn brnn gaussNewton i 20 summary statistics 3.3425 11.1724 2.3126 14.1248 time 0.01
##
## ________________________________________________________________________________
## *** mRef153_CaDENCE::cadence.fit_optim ***
## n.hidden = 3 --> 1 * NLL = -115.459 ; penalty = 0; BIC = -100.1266 ; AICc = -167.7751 ; AIC = -178.9179
## n.hidden = 3 --> 1 * NLL = -134.5653 ; penalty = 0; BIC = -138.3392 ; AICc = -205.9877 ; AIC = -217.1306
## n.hidden = 3 --> 1 * NLL = -126.9515 ; penalty = 0; BIC = -123.1115 ; AICc = -190.7601 ; AIC = -201.9029
## n.hidden = 3 --> 1 * NLL = -118.3101 ; penalty = 0; BIC = -105.8288 ; AICc = -173.4773 ; AIC = -184.6202
## n.hidden = 3 --> 1 * NLL = -125.6982 ; penalty = 0; BIC = -120.6049 ; AICc = -188.2535 ; AIC = -199.3963
## CaDENCE cadence.fit optim i 5 summary statistics 3.4921 12.1945 2.4346 14.1843 time 4.02
## n.hidden = 3 --> 1 * NLL = -112.1768 ; penalty = 0; BIC = -93.56212 ; AICc = -161.2106 ; AIC = -172.3535
## n.hidden = 3 --> 1 * NLL = -110.9977 ; penalty = 0; BIC = -91.2041 ; AICc = -158.8526 ; AIC = -169.9955
## n.hidden = 3 --> 1 * NLL = -126.4222 ; penalty = 0; BIC = -122.053 ; AICc = -189.7016 ; AIC = -200.8444
## n.hidden = 3 --> 1 * NLL = -105.304 ; penalty = 0; BIC = -79.81656 ; AICc = -147.4651 ; AIC = -158.6079
## n.hidden = 3 --> 1 * NLL = -134.5653 ; penalty = 0; BIC = -138.3392 ; AICc = -205.9877 ; AIC = -217.1305
## CaDENCE cadence.fit optim i 10 summary statistics 3.471 12.0482 2.3375 11.9008 time 2.84
## n.hidden = 3 --> 1 * NLL = -126.6715 ; penalty = 0; BIC = -122.5516 ; AICc = -190.2001 ; AIC = -201.343
## n.hidden = 3 --> 1 * NLL = -125.0806 ; penalty = 0; BIC = -119.3698 ; AICc = -187.0183 ; AIC = -198.1611
## n.hidden = 3 --> 1 * NLL = -130.7349 ; penalty = 0; BIC = -130.6785 ; AICc = -198.327 ; AIC = -209.4698
## n.hidden = 3 --> 1 * NLL = -110.2143 ; penalty = 0; BIC = -89.63715 ; AICc = -157.2857 ; AIC = -168.4285
## n.hidden = 3 --> 1 * NLL = -138.3038 ; penalty = 0; BIC = -145.8161 ; AICc = -213.4647 ; AIC = -224.6075
## CaDENCE cadence.fit optim i 15 summary statistics 3.5389 12.5235 2.4037 15.5514 time 3.95
## n.hidden = 3 --> 1 * NLL = -115.3134 ; penalty = 0; BIC = -99.83539 ; AICc = -167.4839 ; AIC = -178.6268
## n.hidden = 3 --> 1 * NLL = -134.5653 ; penalty = 0; BIC = -138.3392 ; AICc = -205.9877 ; AIC = -217.1306
## n.hidden = 3 --> 1 * NLL = -109.7569 ; penalty = 0; BIC = -88.72249 ; AICc = -156.371 ; AIC = -167.5139
## n.hidden = 3 --> 1 * NLL = -133.058 ; penalty = 0; BIC = -135.3246 ; AICc = -202.9731 ; AIC = -214.116
## n.hidden = 3 --> 1 * NLL = -120.5587 ; penalty = 0; BIC = -110.3261 ; AICc = -177.9746 ; AIC = -189.1175
## CaDENCE cadence.fit optim i 20 summary statistics 4.9044 24.0535 3.2858 17.1936 time 4.15
##
## ________________________________________________________________________________
## *** mRef153_MachineShop::fit_none ***
## MachineShop fit none i 5 summary statistics 3.2551 10.5959 2.2679 13.8538 time 0.02
## MachineShop fit none i 10 summary statistics 3.1128 9.6892 2.1804 12.6402 time 0.02
## MachineShop fit none i 15 summary statistics 3.4548 11.9359 2.3838 13.7854 time 0
## MachineShop fit none i 20 summary statistics 3.5675 12.7269 2.6896 14.8891 time 0.03
##
## ________________________________________________________________________________
## *** mRef153_minpack.lm::nlsLM_none ***
## minpack.lm nlsLM none i 5 summary statistics 3.6461 13.294 2.6202 13.931 time 0.08
## minpack.lm nlsLM none i 10 summary statistics 3.1128 9.6892 2.1804 12.6408 time 0.04
## minpack.lm nlsLM none i 15 summary statistics 3.4797 12.1085 2.5205 12.3736 time 0.08
## minpack.lm nlsLM none i 20 summary statistics 3.5666 12.7205 2.6561 14.7303 time 0.08
##
## ________________________________________________________________________________
## *** mRef153_monmlp::monmlp.fit_BFGS ***
## monmlp monmlp.fit BFGS i 5 summary statistics 3.4202 11.6976 2.4653 13.7168 time 0.22
## monmlp monmlp.fit BFGS i 10 summary statistics 3.2499 10.5622 2.3054 14.1438 time 0.22
## monmlp monmlp.fit BFGS i 15 summary statistics 3.2276 10.4176 2.2892 14.4792 time 0.22
## monmlp monmlp.fit BFGS i 20 summary statistics 3.258 10.6148 2.24 13.9283 time 0.22
##
## ________________________________________________________________________________
## *** mRef153_nlsr::nlxb_none ***
## 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
## nlsr nlxb none i 5 summary statistics 3.9374 15.5033 2.8562 14.7413 time 0.15
## 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
## nlsr nlxb none i 10 summary statistics 3.5672 12.7252 2.6815 14.833 time 0.35
## 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
## nlsr nlxb none i 15 summary statistics 3.5963 12.9331 2.7012 15.2347 time 0.15
## 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
## nlsr nlxb none i 20 summary statistics 3.3381 11.1429 2.305 13.0925 time 0.13
##
## ________________________________________________________________________________
## *** mRef153_nnet::nnet_none ***
## nnet nnet none i 5 summary statistics 3.9721 15.7777 2.7401 16.5776 time 0
## nnet nnet none i 10 summary statistics 3.1809 10.1179 2.236 13.4993 time 0.02
## nnet nnet none i 15 summary statistics 3.4619 11.9845 2.5544 14.0788 time 0.02
## nnet nnet none i 20 summary statistics 3.3958 11.5317 2.488 12.8469 time 0.03
##
## ________________________________________________________________________________
## *** mRef153_qrnn::qrnn.fit_none ***
## qrnn qrnn.fit none i 5 summary statistics 3.6014 12.9698 2.1756 17.0007 time 0.15
## qrnn qrnn.fit none i 10 summary statistics 3.3426 11.1727 2.081 15.2132 time 0.17
## qrnn qrnn.fit none i 15 summary statistics 4.1182 16.9597 2.5179 17.0228 time 0.16
## qrnn qrnn.fit none i 20 summary statistics 3.7317 13.9259 2.2474 16.8199 time 0.23
##
## ________________________________________________________________________________
## *** mRef153_radiant.model::nn_none ***
## radiant.model nn none i 5 summary statistics 3.1801 10.113 2.2311 13.536 time 0.03
## radiant.model nn none i 10 summary statistics 3.2259 10.4066 2.1844 14.0203 time 0.04
## radiant.model nn none i 15 summary statistics 3.463 11.9924 2.5041 12.8235 time 0.05
## radiant.model nn none i 20 summary statistics 3.355 11.2562 2.3364 12.8855 time 0.03
##
## ________________________________________________________________________________
## *** mRef153_rminer::fit_none ***
## rminer fit none i 5 summary statistics 3.1128 9.6892 2.1804 12.6402 time 0.05
## rminer fit none i 10 summary statistics 3.3381 11.1429 2.305 13.0921 time 0.05
## rminer fit none i 15 summary statistics 3.1128 9.6892 2.1804 12.6403 time 0.06
## rminer fit none i 20 summary statistics 3.0889 9.5412 2.2317 11.7081 time 0.05
##
## ________________________________________________________________________________
## *** mRef153_validann::ann_BFGS ***
## initial value 176.630972
## iter 20 value 3.343866
## iter 40 value 2.307877
## iter 60 value 2.175648
## iter 80 value 2.103635
## iter 100 value 2.094100
## iter 120 value 2.092913
## final value 2.092913
## converged
## initial value 173.908017
## iter 20 value 3.773445
## iter 40 value 2.843260
## iter 60 value 2.805309
## iter 80 value 2.744566
## iter 100 value 2.702797
## final value 2.702615
## converged
## initial value 213.704507
## iter 20 value 3.927433
## iter 40 value 2.733958
## iter 60 value 2.726571
## iter 80 value 2.670834
## iter 100 value 2.648627
## iter 120 value 2.594176
## iter 140 value 2.394694
## iter 160 value 2.359765
## iter 180 value 2.223469
## iter 200 value 2.193098
## final value 2.193098
## stopped after 200 iterations
## initial value 97.355436
## iter 20 value 3.604907
## iter 40 value 2.701468
## iter 60 value 2.444141
## iter 80 value 2.308551
## iter 100 value 2.206471
## iter 120 value 2.177547
## iter 140 value 2.176299
## iter 160 value 2.176142
## final value 2.176142
## converged
## initial value 170.952686
## iter 20 value 4.939354
## iter 40 value 2.949920
## iter 60 value 2.826389
## iter 80 value 2.713795
## iter 100 value 2.618249
## iter 120 value 2.595735
## iter 140 value 2.591363
## iter 160 value 2.469813
## iter 180 value 2.349549
## iter 200 value 2.325038
## final value 2.325038
## stopped after 200 iterations
## validann ann BFGS i 5 summary statistics 3.4504 11.9053 2.5613 14.4267 time 0.87
## initial value 188.534749
## iter 20 value 2.833500
## iter 40 value 2.449102
## iter 60 value 2.270292
## iter 80 value 2.235847
## iter 100 value 2.234868
## iter 120 value 2.234594
## final value 2.234578
## converged
## initial value 195.953470
## iter 20 value 4.219663
## iter 40 value 2.690943
## iter 60 value 2.269909
## iter 80 value 2.162079
## iter 100 value 2.155137
## iter 120 value 2.140530
## iter 140 value 2.029843
## iter 160 value 2.013567
## iter 180 value 2.013281
## iter 200 value 1.991415
## final value 1.991415
## stopped after 200 iterations
## initial value 203.386810
## iter 20 value 2.767333
## iter 40 value 2.194474
## iter 60 value 2.180555
## iter 80 value 2.176180
## final value 2.176142
## converged
## initial value 140.611186
## iter 20 value 3.497330
## iter 40 value 2.364838
## iter 60 value 2.267775
## iter 80 value 2.183217
## iter 100 value 2.181233
## iter 120 value 2.177690
## iter 140 value 2.176320
## iter 160 value 2.176142
## final value 2.176142
## converged
## initial value 166.654131
## iter 20 value 4.464726
## iter 40 value 2.450097
## iter 60 value 2.406562
## iter 80 value 2.336085
## iter 100 value 2.212217
## iter 120 value 2.181809
## iter 140 value 2.179153
## iter 160 value 2.177001
## iter 180 value 2.176143
## final value 2.176142
## converged
## validann ann BFGS i 10 summary statistics 3.3381 11.1429 2.3052 13.0924 time 0.81
## initial value 201.783312
## iter 20 value 3.403225
## iter 40 value 2.499060
## iter 60 value 2.372871
## iter 80 value 2.356539
## final value 2.356528
## converged
## initial value 267.524212
## iter 20 value 3.866059
## iter 40 value 2.175101
## iter 60 value 2.069595
## iter 80 value 2.003602
## iter 100 value 1.992734
## iter 120 value 1.983059
## iter 140 value 1.977951
## iter 160 value 1.977016
## iter 180 value 1.974602
## iter 200 value 1.974396
## final value 1.974396
## stopped after 200 iterations
## initial value 196.662968
## iter 20 value 5.063353
## iter 40 value 2.594348
## iter 60 value 2.347036
## iter 80 value 2.207165
## iter 100 value 2.144183
## iter 120 value 2.120724
## iter 140 value 2.079687
## iter 160 value 2.069560
## iter 180 value 2.053477
## iter 200 value 2.046105
## final value 2.046105
## stopped after 200 iterations
## initial value 98.460938
## iter 20 value 3.067162
## iter 40 value 2.536931
## iter 60 value 2.527140
## iter 80 value 2.524065
## final value 2.524043
## converged
## initial value 110.988915
## iter 20 value 3.366617
## iter 40 value 2.180683
## iter 60 value 2.107794
## iter 80 value 2.035497
## iter 100 value 2.006035
## iter 120 value 1.987135
## iter 140 value 1.979486
## iter 160 value 1.978015
## iter 180 value 1.975067
## iter 200 value 1.974822
## final value 1.974822
## stopped after 200 iterations
## validann ann BFGS i 15 summary statistics 3.1799 10.112 2.2337 13.53 time 0.89
## initial value 211.649021
## iter 20 value 3.950248
## iter 40 value 3.269055
## iter 60 value 3.186970
## iter 80 value 3.167204
## iter 100 value 3.162057
## iter 120 value 3.157698
## iter 140 value 3.154509
## iter 160 value 3.153808
## iter 180 value 3.152185
## iter 200 value 3.152074
## final value 3.152074
## stopped after 200 iterations
## initial value 125.729476
## iter 20 value 3.663392
## iter 40 value 2.710617
## iter 60 value 2.421701
## iter 80 value 2.343931
## iter 100 value 2.265967
## iter 120 value 2.251899
## iter 140 value 2.251750
## iter 160 value 2.251711
## final value 2.251619
## converged
## initial value 173.438379
## iter 20 value 4.602264
## iter 40 value 3.018562
## iter 60 value 2.551140
## iter 80 value 2.488647
## iter 100 value 2.467883
## iter 120 value 2.453345
## iter 140 value 2.429981
## iter 160 value 2.421260
## iter 180 value 2.403975
## iter 200 value 2.397904
## final value 2.397904
## stopped after 200 iterations
## initial value 146.706211
## iter 20 value 4.448531
## iter 40 value 2.825821
## iter 60 value 2.763089
## iter 80 value 2.699919
## iter 100 value 2.675903
## iter 120 value 2.646123
## iter 140 value 2.623031
## iter 160 value 2.591559
## iter 180 value 2.571690
## iter 200 value 2.561654
## final value 2.561654
## stopped after 200 iterations
## initial value 168.269180
## iter 20 value 4.402175
## iter 40 value 3.291798
## iter 60 value 2.263046
## iter 80 value 2.132806
## iter 100 value 2.112579
## iter 120 value 2.094248
## iter 140 value 2.093173
## iter 160 value 2.092913
## final value 2.092913
## converged
## validann ann BFGS i 20 summary statistics 3.2736 10.7167 2.2964 14.6791 time 0.72
##
## ________________________________________________________________________________
## *** mRef153_validann::ann_L-BFGS-B ***
## iter 20 value 3.313875
## iter 40 value 2.757808
## iter 60 value 2.623897
## iter 80 value 2.565187
## iter 100 value 2.538363
## iter 120 value 2.517743
## iter 140 value 2.512144
## iter 160 value 2.504329
## iter 180 value 2.499866
## iter 200 value 2.496488
## final value 2.496324
## stopped after 201 iterations
## iter 20 value 3.875241
## iter 40 value 3.056446
## iter 60 value 2.577696
## iter 80 value 2.203411
## iter 100 value 2.047233
## iter 120 value 1.958431
## iter 140 value 1.915843
## iter 160 value 1.898411
## iter 180 value 1.893559
## iter 200 value 1.883522
## final value 1.883427
## stopped after 201 iterations
## iter 20 value 3.531614
## iter 40 value 2.765328
## iter 60 value 2.622180
## iter 80 value 2.549429
## iter 100 value 2.527263
## iter 120 value 2.509815
## iter 140 value 2.504387
## iter 160 value 2.500422
## iter 180 value 2.497061
## iter 200 value 2.491799
## final value 2.491703
## stopped after 201 iterations
## iter 20 value 3.836585
## iter 40 value 3.165460
## iter 60 value 2.906296
## iter 80 value 2.881877
## iter 100 value 2.867123
## iter 120 value 2.859943
## iter 140 value 2.857102
## iter 160 value 2.854616
## iter 180 value 2.852940
## iter 200 value 2.851027
## final value 2.850969
## stopped after 201 iterations
## iter 20 value 3.222385
## iter 40 value 2.531548
## iter 60 value 2.196637
## iter 80 value 2.134528
## iter 100 value 2.097746
## iter 120 value 2.075915
## iter 140 value 2.054563
## iter 160 value 2.036071
## iter 180 value 2.019603
## iter 200 value 2.009312
## final value 2.008567
## stopped after 201 iterations
## validann ann L-BFGS-B i 5 summary statistics 3.207 10.2848 2.2418 14.1348 time 0.95
## iter 20 value 3.365992
## iter 40 value 2.892077
## iter 60 value 2.741140
## iter 80 value 2.599744
## iter 100 value 2.552948
## iter 120 value 2.534464
## iter 140 value 2.518205
## iter 160 value 2.509165
## iter 180 value 2.505849
## iter 200 value 2.498180
## final value 2.497782
## stopped after 201 iterations
## iter 20 value 3.639928
## iter 40 value 3.387679
## iter 60 value 3.346408
## iter 80 value 3.242460
## iter 100 value 2.987880
## iter 120 value 2.855011
## iter 140 value 2.715469
## iter 160 value 2.593690
## iter 180 value 2.547714
## iter 200 value 2.527336
## final value 2.526903
## stopped after 201 iterations
## iter 20 value 3.619281
## iter 40 value 3.296909
## iter 60 value 3.077651
## iter 80 value 3.004362
## iter 100 value 2.931660
## iter 120 value 2.851246
## iter 140 value 2.820301
## iter 160 value 2.796210
## iter 180 value 2.772104
## iter 200 value 2.718538
## final value 2.714440
## stopped after 201 iterations
## iter 20 value 3.257063
## iter 40 value 2.475589
## iter 60 value 2.198642
## iter 80 value 2.144691
## iter 100 value 2.130633
## iter 120 value 2.114757
## iter 140 value 2.090939
## iter 160 value 2.061436
## iter 180 value 2.040125
## iter 200 value 2.027956
## final value 2.027368
## stopped after 201 iterations
## iter 20 value 3.243703
## iter 40 value 2.396329
## iter 60 value 2.142844
## iter 80 value 2.102343
## iter 100 value 2.093355
## iter 120 value 2.082142
## iter 140 value 2.070857
## iter 160 value 2.059838
## iter 180 value 2.056221
## iter 200 value 2.054001
## final value 2.053940
## stopped after 201 iterations
## validann ann L-BFGS-B i 10 summary statistics 3.243 10.5172 2.1981 14.3862 time 0.94
## iter 20 value 3.205330
## iter 40 value 2.636821
## iter 60 value 2.468782
## iter 80 value 2.409403
## iter 100 value 2.364487
## iter 120 value 2.354317
## iter 140 value 2.348967
## iter 160 value 2.344120
## iter 180 value 2.312437
## iter 200 value 2.282819
## final value 2.281819
## stopped after 201 iterations
## iter 20 value 2.965635
## iter 40 value 2.675007
## iter 60 value 2.354609
## iter 80 value 2.168677
## iter 100 value 2.141697
## iter 120 value 2.125781
## iter 140 value 2.080076
## iter 160 value 2.059114
## iter 180 value 2.038574
## iter 200 value 2.024454
## final value 2.023348
## stopped after 201 iterations
## iter 20 value 3.490650
## iter 40 value 3.116554
## iter 60 value 2.981455
## iter 80 value 2.909861
## iter 100 value 2.868967
## iter 120 value 2.814343
## iter 140 value 2.766777
## iter 160 value 2.742555
## iter 180 value 2.735840
## iter 200 value 2.729819
## final value 2.729605
## stopped after 201 iterations
## iter 20 value 3.603455
## iter 40 value 3.282168
## iter 60 value 2.771793
## iter 80 value 2.585011
## iter 100 value 2.543757
## iter 120 value 2.518781
## iter 140 value 2.509760
## iter 160 value 2.502478
## iter 180 value 2.501210
## iter 200 value 2.498508
## final value 2.498334
## stopped after 201 iterations
## iter 20 value 3.457034
## iter 40 value 2.804051
## iter 60 value 2.654393
## iter 80 value 2.533081
## iter 100 value 2.510521
## iter 120 value 2.502693
## iter 140 value 2.500452
## iter 160 value 2.497768
## iter 180 value 2.495494
## iter 200 value 2.492828
## final value 2.492745
## stopped after 201 iterations
## validann ann L-BFGS-B i 15 summary statistics 3.5727 12.7641 2.6665 14.9103 time 0.89
## iter 20 value 3.380652
## iter 40 value 2.786750
## iter 60 value 2.593805
## iter 80 value 2.530952
## iter 100 value 2.515228
## iter 120 value 2.510485
## iter 140 value 2.507614
## iter 160 value 2.500159
## iter 180 value 2.493581
## iter 200 value 2.491427
## final value 2.491362
## stopped after 201 iterations
## iter 20 value 4.123067
## iter 40 value 3.184311
## iter 60 value 2.636557
## iter 80 value 2.381323
## iter 100 value 2.248286
## iter 120 value 2.199739
## iter 140 value 2.186318
## iter 160 value 2.182012
## iter 180 value 2.179392
## iter 200 value 2.177850
## final value 2.177823
## stopped after 201 iterations
## iter 20 value 3.468028
## iter 40 value 3.080248
## iter 60 value 2.589917
## iter 80 value 2.496231
## iter 100 value 2.466755
## iter 120 value 2.450573
## iter 140 value 2.440022
## iter 160 value 2.392735
## iter 180 value 2.254159
## iter 200 value 2.209038
## final value 2.208094
## stopped after 201 iterations
## iter 20 value 3.633123
## iter 40 value 2.697546
## iter 60 value 2.335877
## iter 80 value 2.217085
## iter 100 value 2.196757
## iter 120 value 2.185360
## iter 140 value 2.183395
## iter 160 value 2.181739
## iter 180 value 2.180320
## iter 200 value 2.179145
## final value 2.179027
## stopped after 201 iterations
## iter 20 value 3.311525
## iter 40 value 2.844990
## iter 60 value 2.528480
## iter 80 value 2.414970
## iter 100 value 2.328134
## iter 120 value 2.293393
## iter 140 value 2.251571
## iter 160 value 2.203535
## iter 180 value 2.107280
## iter 200 value 1.944881
## final value 1.944001
## stopped after 201 iterations
## validann ann L-BFGS-B i 20 summary statistics 3.155 9.9542 2.1962 13.4268 time 0.95
##
## ________________________________________________________________________________
## *** uDmod1_brnn::brnn_gaussNewton ***
## Number of parameters (weights and biases) to estimate: 18
## Nguyen-Widrow method
## Scaling factor= 4.2
## gamma= 0 alpha= 34.1515 beta= 0.51
## Number of parameters (weights and biases) to estimate: 18
## Nguyen-Widrow method
## Scaling factor= 4.2
## gamma= 0 alpha= 29.0204 beta= 0.51
## Number of parameters (weights and biases) to estimate: 18
## Nguyen-Widrow method
## Scaling factor= 4.2
## gamma= 16.5606 alpha= 0.0242 beta= 58.6801
## Number of parameters (weights and biases) to estimate: 18
## Nguyen-Widrow method
## Scaling factor= 4.2
## gamma= 16.5652 alpha= 0.0243 beta= 58.5974
## Number of parameters (weights and biases) to estimate: 18
## Nguyen-Widrow method
## Scaling factor= 4.2
## gamma= 16.5664 alpha= 0.0243 beta= 58.5845
## brnn brnn gaussNewton i 5 summary statistics 0.0451 0.002 0.0364 0.1168 time 0
## Number of parameters (weights and biases) to estimate: 18
## Nguyen-Widrow method
## Scaling factor= 4.2
## gamma= 16.5654 alpha= 0.0243 beta= 58.5844
## Number of parameters (weights and biases) to estimate: 18
## Nguyen-Widrow method
## Scaling factor= 4.2
## gamma= 16.5607 alpha= 0.0242 beta= 58.6798
## Number of parameters (weights and biases) to estimate: 18
## Nguyen-Widrow method
## Scaling factor= 4.2
## gamma= 16.5653 alpha= 0.0243 beta= 58.5869
## Number of parameters (weights and biases) to estimate: 18
## Nguyen-Widrow method
## Scaling factor= 4.2
## gamma= 16.5657 alpha= 0.0243 beta= 58.5778
## Number of parameters (weights and biases) to estimate: 18
## Nguyen-Widrow method
## Scaling factor= 4.2
## gamma= 0 alpha= 30.1509 beta= 0.51
## brnn brnn gaussNewton i 10 summary statistics 0.5884 0.3462 0.5069 1.0104 time 0.02
## Number of parameters (weights and biases) to estimate: 18
## Nguyen-Widrow method
## Scaling factor= 4.2
## gamma= 16.5609 alpha= 0.0242 beta= 58.6787
## Number of parameters (weights and biases) to estimate: 18
## Nguyen-Widrow method
## Scaling factor= 4.2
## gamma= 16.5631 alpha= 0.0242 beta= 58.6653
## Number of parameters (weights and biases) to estimate: 18
## Nguyen-Widrow method
## Scaling factor= 4.2
## gamma= 16.5655 alpha= 0.0243 beta= 58.5854
## 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= 28.25 beta= 0.51
## brnn brnn gaussNewton i 15 summary statistics 0.5884 0.3462 0.5069 1.0104 time 0
## Number of parameters (weights and biases) to estimate: 18
## Nguyen-Widrow method
## Scaling factor= 4.2
## gamma= 16.561 alpha= 0.0242 beta= 58.6783
## Number of parameters (weights and biases) to estimate: 18
## Nguyen-Widrow method
## Scaling factor= 4.2
## gamma= 0 alpha= 58.5209 beta= 0.51
## Number of parameters (weights and biases) to estimate: 18
## Nguyen-Widrow method
## Scaling factor= 4.2
## gamma= 16.5608 alpha= 0.0242 beta= 58.6798
## Number of parameters (weights and biases) to estimate: 18
## Nguyen-Widrow method
## Scaling factor= 4.2
## gamma= 15.6208 alpha= 0.0201 beta= 56.0609
## Number of parameters (weights and biases) to estimate: 18
## Nguyen-Widrow method
## Scaling factor= 4.2
## gamma= 16.5652 alpha= 0.0243 beta= 58.5825
## brnn brnn gaussNewton i 20 summary statistics 0.0451 0.002 0.0364 0.1167 time 0.02
##
## ________________________________________________________________________________
## *** uDmod1_CaDENCE::cadence.fit_optim ***
## n.hidden = 6 --> 1 * NLL = -24.88575 ; penalty = 0; BIC = 52.45596 ; AICc = 60.72849 ; AIC = 2.228492
## n.hidden = 6 --> 1 * NLL = -48.08681 ; penalty = 0; BIC = 6.053843 ; AICc = 14.32638 ; AIC = -44.17362
## n.hidden = 6 --> 1 * NLL = -45.13211 ; penalty = 0; BIC = 11.96326 ; AICc = 20.23579 ; AIC = -38.26421
## n.hidden = 6 --> 1 * NLL = -50.7418 ; penalty = 0; BIC = 0.7438761 ; AICc = 9.01641 ; AIC = -49.48359
## n.hidden = 6 --> 1 * NLL = -34.89961 ; penalty = 0; BIC = 32.42825 ; AICc = 40.70078 ; AIC = -17.79922
## CaDENCE cadence.fit optim i 5 summary statistics 0.2515 0.0633 0.1175 1.0612 time 3
## n.hidden = 6 --> 1 * NLL = -41.62793 ; penalty = 0; BIC = 18.97161 ; AICc = 27.24414 ; AIC = -31.25586
## n.hidden = 6 --> 1 * NLL = -69.0978 ; penalty = 0; BIC = -35.96814 ; AICc = -27.6956 ; AIC = -86.1956
## n.hidden = 6 --> 1 * NLL = -27.54008 ; penalty = 0; BIC = 47.14732 ; AICc = 55.41985 ; AIC = -3.08015
## n.hidden = 6 --> 1 * NLL = -17.02702 ; penalty = 0; BIC = 68.17342 ; AICc = 76.44595 ; AIC = 17.94595
## n.hidden = 6 --> 1 * NLL = -44.80242 ; penalty = 0; BIC = 12.62262 ; AICc = 20.89516 ; AIC = -37.60484
## CaDENCE cadence.fit optim i 10 summary statistics 0.1619 0.0262 0.0878 0.7478 time 2.64
## n.hidden = 6 --> 1 * NLL = -31.59806 ; penalty = 0; BIC = 39.03135 ; AICc = 47.30388 ; AIC = -11.19612
## n.hidden = 6 --> 1 * NLL = -7.367879 ; penalty = 0; BIC = 87.49171 ; AICc = 95.76424 ; AIC = 37.26424
## n.hidden = 6 --> 1 * NLL = -36.23486 ; penalty = 0; BIC = 29.75775 ; AICc = 38.03028 ; AIC = -20.46972
## n.hidden = 6 --> 1 * NLL = 7.961892 ; penalty = 0; BIC = 118.1512 ; AICc = 126.4238 ; AIC = 67.92378
## n.hidden = 6 --> 1 * NLL = -40.75285 ; penalty = 0; BIC = 20.72176 ; AICc = 28.9943 ; AIC = -29.5057
## CaDENCE cadence.fit optim i 15 summary statistics 0.1457 0.0212 0.0793 0.6902 time 2.5
## n.hidden = 6 --> 1 * NLL = -57.87136 ; penalty = 0; BIC = -13.51525 ; AICc = -5.242712 ; AIC = -63.74271
## n.hidden = 6 --> 1 * NLL = -22.09694 ; penalty = 0; BIC = 58.03359 ; AICc = 66.30612 ; AIC = 7.806122
## n.hidden = 6 --> 1 * NLL = -31.54708 ; penalty = 0; BIC = 39.13331 ; AICc = 47.40585 ; AIC = -11.09415
## n.hidden = 6 --> 1 * NLL = -40.82764 ; penalty = 0; BIC = 20.57218 ; AICc = 28.84472 ; AIC = -29.65528
## n.hidden = 6 --> 1 * NLL = -34.93101 ; penalty = 0; BIC = 32.36545 ; AICc = 40.63798 ; AIC = -17.86202
## CaDENCE cadence.fit optim i 20 summary statistics 0.1469 0.0216 0.0987 0.4319 time 2.68
##
## ________________________________________________________________________________
## *** uDmod1_MachineShop::fit_none ***
## MachineShop fit none i 5 summary statistics 0.1095 0.012 0.0778 0.4137 time 0.01
## MachineShop fit none i 10 summary statistics 0.0451 0.002 0.0364 0.1048 time 0.02
## MachineShop fit none i 15 summary statistics 0.0612 0.0037 0.0477 0.203 time 0.02
## MachineShop fit none i 20 summary statistics 0.1193 0.0142 0.0793 0.4916 time 0.01
##
## ________________________________________________________________________________
## *** uDmod1_minpack.lm::nlsLM_none ***
## minpack.lm nlsLM none i 5 summary statistics 0.0689 0.0048 0.0535 0.235 time 0.03
## minpack.lm nlsLM none i 10 summary statistics 0.0433 0.0019 0.0349 0.1063 time 0.03
## minpack.lm nlsLM none i 15 summary statistics 0.043 0.0018 0.0344 0.1095 time 0.06
## minpack.lm nlsLM none i 20 summary statistics 0.0722 0.0052 0.0585 0.1785 time 0.03
##
## ________________________________________________________________________________
## *** uDmod1_monmlp::monmlp.fit_BFGS ***
## monmlp monmlp.fit BFGS i 5 summary statistics 0.1246 0.0155 0.0957 0.3565 time 0.22
## monmlp monmlp.fit BFGS i 10 summary statistics 0.1204 0.0145 0.0896 0.4461 time 0.21
## monmlp monmlp.fit BFGS i 15 summary statistics 0.1048 0.011 0.0891 0.3096 time 0.19
## monmlp monmlp.fit BFGS i 20 summary statistics 0.0821 0.0067 0.0673 0.2834 time 0.25
##
## ________________________________________________________________________________
## *** uDmod1_nlsr::nlxb_none ***
## 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
## nlsr nlxb none i 5 summary statistics 0.0579 0.0034 0.0443 0.1721 time 0.09
## 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
## nlsr nlxb none i 10 summary statistics 0.0461 0.0021 0.0364 0.1189 time 0.09
## 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
## nlsr nlxb none i 15 summary statistics 0.0441 0.0019 0.0362 0.1021 time 0.09
## 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
## nlsr nlxb none i 20 summary statistics 0.1119 0.0125 0.0833 0.3146 time 0.09
##
## ________________________________________________________________________________
## *** uDmod1_nnet::nnet_none ***
## nnet nnet none i 5 summary statistics 0.1132 0.0128 0.0718 0.4881 time 0
## nnet nnet none i 10 summary statistics 0.0863 0.0074 0.0627 0.3317 time 0.02
## nnet nnet none i 15 summary statistics 0.0435 0.0019 0.0354 0.1248 time 0.02
## nnet nnet none i 20 summary statistics 0.1122 0.0126 0.0687 0.4884 time 0
##
## ________________________________________________________________________________
## *** uDmod1_qrnn::qrnn.fit_none ***
## qrnn qrnn.fit none i 5 summary statistics 0.0535 0.0029 0.036 0.1663 time 0.41
## qrnn qrnn.fit none i 10 summary statistics 0.1227 0.015 0.0768 0.5214 time 0.61
## qrnn qrnn.fit none i 15 summary statistics 0.1401 0.0196 0.0903 0.5168 time 0.14
## qrnn qrnn.fit none i 20 summary statistics 0.1553 0.0241 0.1102 0.4551 time 0.24
##
## ________________________________________________________________________________
## *** uDmod1_radiant.model::nn_none ***
## radiant.model nn none i 5 summary statistics 0.057 0.0033 0.0465 0.1523 time 0.03
## radiant.model nn none i 10 summary statistics 0.0769 0.0059 0.06 0.2466 time 0.02
## radiant.model nn none i 15 summary statistics 0.1508 0.0227 0.102 0.6172 time 0.05
## radiant.model nn none i 20 summary statistics 0.1089 0.0119 0.0762 0.4279 time 0.03
##
## ________________________________________________________________________________
## *** uDmod1_rminer::fit_none ***
## rminer fit none i 5 summary statistics 0.0534 0.0029 0.0439 0.1088 time 0
## rminer fit none i 10 summary statistics 0.0445 0.002 0.0364 0.1156 time 0.01
## rminer fit none i 15 summary statistics 0.0458 0.0021 0.0377 0.1018 time 0.03
## rminer fit none i 20 summary statistics 0.0798 0.0064 0.0582 0.2828 time 0.03
##
## ________________________________________________________________________________
## *** uDmod1_validann::ann_BFGS ***
## initial value 50.605062
## iter 20 value 14.536410
## iter 40 value 4.235065
## iter 60 value 2.716399
## iter 80 value 0.930805
## iter 100 value 0.478238
## iter 120 value 0.353499
## iter 140 value 0.312879
## iter 160 value 0.304185
## iter 180 value 0.302656
## iter 200 value 0.297357
## final value 0.297357
## stopped after 200 iterations
## initial value 61.773254
## iter 20 value 8.423978
## iter 40 value 2.842451
## iter 60 value 1.761869
## iter 80 value 0.520736
## iter 100 value 0.418180
## iter 120 value 0.340179
## iter 140 value 0.329011
## iter 160 value 0.321827
## iter 180 value 0.317007
## iter 200 value 0.314373
## final value 0.314373
## stopped after 200 iterations
## initial value 56.991619
## iter 20 value 9.948172
## iter 40 value 2.812370
## iter 60 value 1.959596
## iter 80 value 1.861562
## iter 100 value 1.812437
## iter 120 value 1.746635
## iter 140 value 1.691379
## iter 160 value 1.469416
## iter 180 value 1.087604
## iter 200 value 0.922562
## final value 0.922562
## stopped after 200 iterations
## initial value 49.578807
## iter 20 value 15.727642
## iter 40 value 9.774188
## iter 60 value 5.900893
## iter 80 value 3.642869
## iter 100 value 3.145122
## iter 120 value 2.229385
## iter 140 value 1.932582
## iter 160 value 1.782903
## iter 180 value 1.757624
## iter 200 value 1.715555
## final value 1.715555
## stopped after 200 iterations
## initial value 52.868620
## iter 20 value 13.570223
## iter 40 value 3.924072
## iter 60 value 2.093986
## iter 80 value 1.924245
## iter 100 value 1.903869
## iter 120 value 1.891190
## iter 140 value 1.877535
## iter 160 value 1.820811
## iter 180 value 1.796921
## iter 200 value 1.772493
## final value 1.772493
## stopped after 200 iterations
## validann ann BFGS i 5 summary statistics 0.1108 0.0123 0.0705 0.5086 time 0.72
## initial value 58.140015
## iter 20 value 11.567921
## iter 40 value 6.189123
## iter 60 value 2.565931
## iter 80 value 2.061138
## iter 100 value 1.479493
## iter 120 value 1.337562
## iter 140 value 1.218580
## iter 160 value 1.130701
## iter 180 value 1.085639
## iter 200 value 1.031374
## final value 1.031374
## stopped after 200 iterations
## initial value 49.327516
## iter 20 value 12.367230
## iter 40 value 9.196038
## iter 60 value 2.489655
## iter 80 value 1.860278
## iter 100 value 1.777853
## iter 120 value 1.763886
## iter 140 value 1.763798
## final value 1.763664
## converged
## initial value 54.862720
## iter 20 value 10.326403
## iter 40 value 2.842141
## iter 60 value 2.095670
## iter 80 value 1.570073
## iter 100 value 1.422960
## iter 120 value 0.874418
## iter 140 value 0.554166
## iter 160 value 0.357696
## iter 180 value 0.329174
## iter 200 value 0.287173
## final value 0.287173
## stopped after 200 iterations
## initial value 52.526733
## iter 20 value 15.133399
## iter 40 value 5.287682
## iter 60 value 3.599254
## iter 80 value 0.598547
## iter 100 value 0.420222
## iter 120 value 0.291607
## iter 140 value 0.281393
## iter 160 value 0.274899
## iter 180 value 0.273448
## iter 200 value 0.271844
## final value 0.271844
## stopped after 200 iterations
## initial value 54.394787
## iter 20 value 18.803388
## iter 40 value 8.714750
## iter 60 value 3.401143
## iter 80 value 2.097485
## iter 100 value 1.535717
## iter 120 value 1.147160
## iter 140 value 1.064999
## iter 160 value 0.994987
## iter 180 value 0.973922
## iter 200 value 0.953227
## final value 0.953227
## stopped after 200 iterations
## validann ann BFGS i 10 summary statistics 0.0812 0.0066 0.0595 0.27 time 0.75
## initial value 50.082532
## iter 20 value 11.417359
## iter 40 value 4.716972
## iter 60 value 3.822018
## iter 80 value 2.981876
## iter 100 value 2.177853
## iter 120 value 1.817516
## iter 140 value 1.803087
## iter 160 value 1.795140
## iter 180 value 1.791783
## iter 200 value 1.787620
## final value 1.787620
## stopped after 200 iterations
## initial value 55.125277
## iter 20 value 9.892535
## iter 40 value 4.609921
## iter 60 value 3.992408
## iter 80 value 3.700400
## iter 100 value 2.678814
## iter 120 value 1.945602
## iter 140 value 1.925213
## iter 160 value 1.921656
## iter 180 value 1.921480
## iter 200 value 1.917997
## final value 1.917997
## stopped after 200 iterations
## initial value 68.840959
## iter 20 value 17.731088
## iter 40 value 7.165232
## iter 60 value 3.082337
## iter 80 value 2.620202
## iter 100 value 1.946840
## iter 120 value 1.143440
## iter 140 value 0.578783
## iter 160 value 0.524572
## iter 180 value 0.522264
## iter 200 value 0.519092
## final value 0.519092
## stopped after 200 iterations
## initial value 57.218648
## iter 20 value 12.393722
## iter 40 value 3.217909
## iter 60 value 2.210381
## iter 80 value 2.036580
## iter 100 value 1.919656
## iter 120 value 1.899910
## iter 140 value 1.896629
## iter 160 value 1.884451
## iter 180 value 1.837820
## iter 200 value 1.753711
## final value 1.753711
## stopped after 200 iterations
## initial value 59.554725
## iter 20 value 13.839931
## iter 40 value 3.994697
## iter 60 value 1.857527
## iter 80 value 0.583408
## iter 100 value 0.389841
## iter 120 value 0.326093
## iter 140 value 0.307091
## iter 160 value 0.283955
## iter 180 value 0.279921
## iter 200 value 0.275194
## final value 0.275194
## stopped after 200 iterations
## validann ann BFGS i 15 summary statistics 0.0437 0.0019 0.0357 0.1154 time 0.7
## initial value 79.209217
## iter 20 value 11.056327
## iter 40 value 2.424569
## iter 60 value 1.937603
## iter 80 value 1.903932
## iter 100 value 1.901423
## iter 120 value 1.898262
## iter 140 value 1.896859
## iter 160 value 1.894736
## iter 180 value 1.894453
## final value 1.894404
## converged
## initial value 55.379786
## iter 20 value 9.815512
## iter 40 value 4.201079
## iter 60 value 3.349975
## iter 80 value 2.236609
## iter 100 value 1.461531
## iter 120 value 1.214166
## iter 140 value 1.162133
## iter 160 value 1.077232
## iter 180 value 1.055447
## iter 200 value 1.036759
## final value 1.036759
## stopped after 200 iterations
## initial value 59.024551
## iter 20 value 18.466000
## iter 40 value 2.632991
## iter 60 value 2.237150
## iter 80 value 1.696560
## iter 100 value 1.565903
## iter 120 value 1.117345
## iter 140 value 0.632921
## iter 160 value 0.551844
## iter 180 value 0.544268
## iter 200 value 0.523137
## final value 0.523137
## stopped after 200 iterations
## initial value 52.965438
## iter 20 value 15.382436
## iter 40 value 5.033227
## iter 60 value 1.634532
## iter 80 value 0.414613
## iter 100 value 0.323845
## iter 120 value 0.304309
## iter 140 value 0.292658
## iter 160 value 0.289024
## iter 180 value 0.285242
## iter 200 value 0.283775
## final value 0.283775
## stopped after 200 iterations
## initial value 77.262107
## iter 20 value 18.938884
## iter 40 value 9.763032
## iter 60 value 5.304765
## iter 80 value 0.666209
## iter 100 value 0.544885
## iter 120 value 0.471680
## iter 140 value 0.457726
## iter 160 value 0.456483
## final value 0.456479
## converged
## validann ann BFGS i 20 summary statistics 0.0562 0.0032 0.0432 0.1661 time 0.58
##
## ________________________________________________________________________________
## *** uDmod1_validann::ann_L-BFGS-B ***
## iter 20 value 19.353174
## iter 40 value 8.539962
## iter 60 value 6.209050
## iter 80 value 3.237347
## iter 100 value 1.695693
## iter 120 value 1.152113
## iter 140 value 0.702253
## iter 160 value 0.632475
## iter 180 value 0.500768
## iter 200 value 0.403355
## final value 0.402658
## stopped after 201 iterations
## iter 20 value 13.920219
## iter 40 value 6.853084
## iter 60 value 5.134633
## iter 80 value 2.759380
## iter 100 value 1.545548
## iter 120 value 0.879339
## iter 140 value 0.665335
## iter 160 value 0.567099
## iter 180 value 0.510308
## iter 200 value 0.394926
## final value 0.394692
## stopped after 201 iterations
## iter 20 value 17.402208
## iter 40 value 6.453371
## iter 60 value 2.179457
## iter 80 value 1.934184
## iter 100 value 1.481517
## iter 120 value 0.845467
## iter 140 value 0.712231
## iter 160 value 0.591517
## iter 180 value 0.566605
## iter 200 value 0.524060
## final value 0.523989
## stopped after 201 iterations
## iter 20 value 18.322582
## iter 40 value 9.412717
## iter 60 value 4.516885
## iter 80 value 2.954824
## iter 100 value 2.788556
## iter 120 value 2.209770
## iter 140 value 2.054494
## iter 160 value 2.002262
## iter 180 value 1.972612
## iter 200 value 1.912935
## final value 1.910936
## stopped after 201 iterations
## iter 20 value 18.782212
## iter 40 value 6.826139
## iter 60 value 5.626735
## iter 80 value 4.903141
## iter 100 value 3.091299
## iter 120 value 2.823353
## iter 140 value 2.748450
## iter 160 value 2.377297
## iter 180 value 2.195337
## iter 200 value 2.083086
## final value 2.076391
## stopped after 201 iterations
## validann ann L-BFGS-B i 5 summary statistics 0.1199 0.0144 0.0881 0.4662 time 0.78
## iter 20 value 15.713449
## iter 40 value 8.483121
## iter 60 value 5.704319
## iter 80 value 3.213999
## iter 100 value 2.313435
## iter 120 value 1.667542
## iter 140 value 1.148139
## iter 160 value 0.716661
## iter 180 value 0.557602
## iter 200 value 0.470604
## final value 0.470463
## stopped after 201 iterations
## iter 20 value 19.088792
## iter 40 value 9.547516
## iter 60 value 8.019468
## iter 80 value 6.033537
## iter 100 value 3.158052
## iter 120 value 2.868308
## iter 140 value 2.344139
## iter 160 value 1.985537
## iter 180 value 1.532470
## iter 200 value 1.020403
## final value 1.014428
## stopped after 201 iterations
## iter 20 value 16.406467
## iter 40 value 4.866317
## iter 60 value 2.961239
## iter 80 value 2.868738
## iter 100 value 2.739548
## iter 120 value 2.565610
## iter 140 value 2.328791
## iter 160 value 2.218751
## iter 180 value 2.080000
## iter 200 value 2.028769
## final value 2.025802
## stopped after 201 iterations
## iter 20 value 19.489405
## iter 40 value 8.451657
## iter 60 value 6.843780
## iter 80 value 4.501033
## iter 100 value 4.193753
## iter 120 value 3.949909
## iter 140 value 3.174423
## iter 160 value 2.850607
## iter 180 value 2.448345
## iter 200 value 2.414682
## final value 2.412888
## stopped after 201 iterations
## iter 20 value 12.902087
## iter 40 value 5.637221
## iter 60 value 3.005056
## iter 80 value 2.436770
## iter 100 value 2.250217
## iter 120 value 2.116073
## iter 140 value 2.052680
## iter 160 value 1.984499
## iter 180 value 1.979527
## iter 200 value 1.960128
## final value 1.956329
## stopped after 201 iterations
## validann ann L-BFGS-B i 10 summary statistics 0.1164 0.0135 0.0771 0.4984 time 0.8
## iter 20 value 18.734054
## iter 40 value 6.372452
## iter 60 value 4.954115
## iter 80 value 3.054597
## iter 100 value 2.859621
## iter 120 value 2.681183
## iter 140 value 1.707211
## iter 160 value 1.530903
## iter 180 value 1.136985
## iter 200 value 0.642827
## final value 0.638279
## stopped after 201 iterations
## iter 20 value 17.736036
## iter 40 value 10.176404
## iter 60 value 7.374665
## iter 80 value 4.938617
## iter 100 value 3.511505
## iter 120 value 3.356683
## iter 140 value 3.106176
## iter 160 value 2.955146
## iter 180 value 2.753865
## iter 200 value 2.638143
## final value 2.631676
## stopped after 201 iterations
## iter 20 value 13.790214
## iter 40 value 8.405268
## iter 60 value 4.670511
## iter 80 value 2.544882
## iter 100 value 2.282069
## iter 120 value 2.160797
## iter 140 value 2.071798
## iter 160 value 1.950120
## iter 180 value 1.925941
## iter 200 value 1.913176
## final value 1.912981
## stopped after 201 iterations
## iter 20 value 17.986962
## iter 40 value 10.418783
## iter 60 value 7.284360
## iter 80 value 4.354992
## iter 100 value 4.002349
## iter 120 value 3.294038
## iter 140 value 1.394214
## iter 160 value 0.878741
## iter 180 value 0.787096
## iter 200 value 0.657165
## final value 0.652729
## stopped after 201 iterations
## iter 20 value 19.916455
## iter 40 value 9.565325
## iter 60 value 5.881009
## iter 80 value 3.679318
## iter 100 value 3.156748
## iter 120 value 3.020673
## iter 140 value 2.973799
## iter 160 value 2.919311
## iter 180 value 2.837070
## iter 200 value 2.590367
## final value 2.572790
## stopped after 201 iterations
## validann ann L-BFGS-B i 15 summary statistics 0.1335 0.0178 0.097 0.491 time 0.77
## iter 20 value 19.822660
## iter 40 value 15.379977
## iter 60 value 9.142790
## iter 80 value 6.751846
## iter 100 value 4.593954
## iter 120 value 3.588166
## iter 140 value 3.206527
## iter 160 value 2.987776
## iter 180 value 2.917607
## iter 200 value 2.822399
## final value 2.806014
## stopped after 201 iterations
## iter 20 value 14.547127
## iter 40 value 7.970014
## iter 60 value 4.316494
## iter 80 value 3.026310
## iter 100 value 2.878757
## iter 120 value 2.603916
## iter 140 value 2.449512
## iter 160 value 2.315041
## iter 180 value 2.222892
## iter 200 value 2.135493
## final value 2.133735
## stopped after 201 iterations
## iter 20 value 21.022526
## iter 40 value 9.245495
## iter 60 value 8.582156
## iter 80 value 8.091324
## iter 100 value 5.430789
## iter 120 value 3.882321
## iter 140 value 2.352907
## iter 160 value 1.964811
## iter 180 value 1.672176
## iter 200 value 1.304322
## final value 1.298233
## stopped after 201 iterations
## iter 20 value 20.284809
## iter 40 value 17.142635
## iter 60 value 9.780966
## iter 80 value 7.096100
## iter 100 value 4.991467
## iter 120 value 3.438380
## iter 140 value 3.006228
## iter 160 value 2.129467
## iter 180 value 1.878593
## iter 200 value 1.742383
## final value 1.734589
## stopped after 201 iterations
## iter 20 value 8.829650
## iter 40 value 6.676793
## iter 60 value 4.950651
## iter 80 value 3.794133
## iter 100 value 3.131186
## iter 120 value 2.313836
## iter 140 value 2.200824
## iter 160 value 2.172079
## iter 180 value 2.135682
## iter 200 value 2.108615
## final value 2.107660
## stopped after 201 iterations
## validann ann L-BFGS-B i 20 summary statistics 0.1208 0.0146 0.0766 0.5155 time 0.78
##
## ________________________________________________________________________________
## *** uDmod2_brnn::brnn_gaussNewton ***
## Number of parameters (weights and biases) to estimate: 15
## Nguyen-Widrow method
## Scaling factor= 3.5
## gamma= 13.453 alpha= 0.0206 beta= 26.4203
## Number of parameters (weights and biases) to estimate: 15
## Nguyen-Widrow method
## Scaling factor= 3.5
## gamma= 13.8571 alpha= 0.0301 beta= 52.6602
## Number of parameters (weights and biases) to estimate: 15
## Nguyen-Widrow method
## Scaling factor= 3.5
## gamma= 13.8576 alpha= 0.0301 beta= 52.6588
## Number of parameters (weights and biases) to estimate: 15
## Nguyen-Widrow method
## Scaling factor= 3.5
## gamma= 13.8589 alpha= 0.0301 beta= 52.6544
## Number of parameters (weights and biases) to estimate: 15
## Nguyen-Widrow method
## Scaling factor= 3.5
## gamma= 13.8575 alpha= 0.0301 beta= 52.6593
## brnn brnn gaussNewton i 5 summary statistics 0.0435 0.0019 0.0346 0.1073 time 0.03
## Number of parameters (weights and biases) to estimate: 15
## Nguyen-Widrow method
## Scaling factor= 3.5
## gamma= 12.9013 alpha= 0.0271 beta= 23.0052
## Number of parameters (weights and biases) to estimate: 15
## Nguyen-Widrow method
## Scaling factor= 3.5
## gamma= 13.858 alpha= 0.0301 beta= 52.6574
## Number of parameters (weights and biases) to estimate: 15
## Nguyen-Widrow method
## Scaling factor= 3.5
## gamma= 12.7342 alpha= 0.0265 beta= 23.1628
## Number of parameters (weights and biases) to estimate: 15
## Nguyen-Widrow method
## Scaling factor= 3.5
## gamma= 13.8573 alpha= 0.0301 beta= 52.6596
## Number of parameters (weights and biases) to estimate: 15
## Nguyen-Widrow method
## Scaling factor= 3.5
## gamma= 13.8584 alpha= 0.0301 beta= 52.6561
## brnn brnn gaussNewton i 10 summary statistics 0.0435 0.0019 0.0346 0.1073 time 0.02
## Number of parameters (weights and biases) to estimate: 15
## Nguyen-Widrow method
## Scaling factor= 3.5
## gamma= 13.857 alpha= 0.0301 beta= 52.6605
## Number of parameters (weights and biases) to estimate: 15
## Nguyen-Widrow method
## Scaling factor= 3.5
## gamma= 13.859 alpha= 0.0301 beta= 52.6543
## Number of parameters (weights and biases) to estimate: 15
## Nguyen-Widrow method
## Scaling factor= 3.5
## gamma= 12.0072 alpha= 0.0397 beta= 16.2077
## Number of parameters (weights and biases) to estimate: 15
## Nguyen-Widrow method
## Scaling factor= 3.5
## gamma= 12.898 alpha= 0.0271 beta= 23.0008
## Number of parameters (weights and biases) to estimate: 15
## Nguyen-Widrow method
## Scaling factor= 3.5
## gamma= 13.8575 alpha= 0.0301 beta= 52.6589
## brnn brnn gaussNewton i 15 summary statistics 0.0435 0.0019 0.0346 0.1073 time 0.02
## Number of parameters (weights and biases) to estimate: 15
## Nguyen-Widrow method
## Scaling factor= 3.5
## gamma= 12.6857 alpha= 0.0263 beta= 23.227
## Number of parameters (weights and biases) to estimate: 15
## Nguyen-Widrow method
## Scaling factor= 3.5
## gamma= 12.8888 alpha= 0.0272 beta= 22.973
## Number of parameters (weights and biases) to estimate: 15
## Nguyen-Widrow method
## Scaling factor= 3.5
## gamma= 12.8575 alpha= 0.0269 beta= 23.0483
## Number of parameters (weights and biases) to estimate: 15
## Nguyen-Widrow method
## Scaling factor= 3.5
## gamma= 13.8577 alpha= 0.0301 beta= 52.6584
## Number of parameters (weights and biases) to estimate: 15
## Nguyen-Widrow method
## Scaling factor= 3.5
## gamma= 12.8064 alpha= 0.0266 beta= 23.1257
## brnn brnn gaussNewton i 20 summary statistics 0.0665 0.0044 0.0535 0.1493 time 0.03
##
## ________________________________________________________________________________
## *** uDmod2_CaDENCE::cadence.fit_optim ***
## n.hidden = 5 --> 1 * NLL = -9.108294 ; penalty = 0; BIC = 68.28358 ; AICc = 61.92627 ; AIC = 25.78341
## n.hidden = 5 --> 1 * NLL = -29.36071 ; penalty = 0; BIC = 27.77875 ; AICc = 21.42144 ; AIC = -14.72141
## n.hidden = 5 --> 1 * NLL = -46.34667 ; penalty = 0; BIC = -6.193186 ; AICc = -12.55049 ; AIC = -48.69335
## n.hidden = 5 --> 1 * NLL = -39.9686 ; penalty = 0; BIC = 6.562955 ; AICc = 0.2056484 ; AIC = -35.93721
## n.hidden = 5 --> 1 * NLL = -54.50113 ; penalty = 0; BIC = -22.5021 ; AICc = -28.8594 ; AIC = -65.00226
## CaDENCE cadence.fit optim i 5 summary statistics 0.0766 0.0059 0.0549 0.2212 time 2.32
## n.hidden = 5 --> 1 * NLL = -37.31526 ; penalty = 0; BIC = 11.86964 ; AICc = 5.512333 ; AIC = -30.63052
## n.hidden = 5 --> 1 * NLL = -61.38864 ; penalty = 0; BIC = -36.27713 ; AICc = -42.63443 ; AIC = -78.77729
## n.hidden = 5 --> 1 * NLL = -10.23273 ; penalty = 0; BIC = 66.03471 ; AICc = 59.6774 ; AIC = 23.53455
## n.hidden = 5 --> 1 * NLL = -41.12056 ; penalty = 0; BIC = 4.259049 ; AICc = -2.098258 ; AIC = -38.24111
## n.hidden = 5 --> 1 * NLL = -48.01639 ; penalty = 0; BIC = -9.53262 ; AICc = -15.88993 ; AIC = -52.03278
## CaDENCE cadence.fit optim i 10 summary statistics 0.0678 0.0046 0.0494 0.1789 time 2.29
## n.hidden = 5 --> 1 * NLL = -32.33007 ; penalty = 0; BIC = 21.84003 ; AICc = 15.48273 ; AIC = -20.66013
## n.hidden = 5 --> 1 * NLL = -15.26309 ; penalty = 0; BIC = 55.97398 ; AICc = 49.61667 ; AIC = 13.47381
## n.hidden = 5 --> 1 * NLL = -53.40436 ; penalty = 0; BIC = -20.30855 ; AICc = -26.66585 ; AIC = -62.80871
## n.hidden = 5 --> 1 * NLL = -19.81323 ; penalty = 0; BIC = 46.8737 ; AICc = 40.51639 ; AIC = 4.373533
## n.hidden = 5 --> 1 * NLL = -44.55316 ; penalty = 0; BIC = -2.60615 ; AICc = -8.963456 ; AIC = -45.10631
## CaDENCE cadence.fit optim i 15 summary statistics 0.0868 0.0075 0.0595 0.2692 time 2.33
## n.hidden = 5 --> 1 * NLL = -41.79861 ; penalty = 0; BIC = 2.902949 ; AICc = -3.454357 ; AIC = -39.59721
## n.hidden = 5 --> 1 * NLL = -40.54372 ; penalty = 0; BIC = 5.412728 ; AICc = -0.9445788 ; AIC = -37.08744
## n.hidden = 5 --> 1 * NLL = -43.17527 ; penalty = 0; BIC = 0.1496334 ; AICc = -6.207673 ; AIC = -42.35053
## n.hidden = 5 --> 1 * NLL = -41.66048 ; penalty = 0; BIC = 3.179206 ; AICc = -3.178101 ; AIC = -39.32096
## n.hidden = 5 --> 1 * NLL = -34.00591 ; penalty = 0; BIC = 18.48834 ; AICc = 12.13104 ; AIC = -24.01182
## CaDENCE cadence.fit optim i 20 summary statistics 0.1225 0.015 0.0806 0.4314 time 2.31
##
## ________________________________________________________________________________
## *** uDmod2_MachineShop::fit_none ***
## MachineShop fit none i 5 summary statistics 0.0615 0.0038 0.049 0.1405 time 0.02
## MachineShop fit none i 10 summary statistics 0.0746 0.0056 0.0594 0.2139 time 0
## MachineShop fit none i 15 summary statistics 0.0606 0.0037 0.0475 0.1351 time 0.02
## MachineShop fit none i 20 summary statistics 0.0717 0.0051 0.0538 0.2165 time 0.01
##
## ________________________________________________________________________________
## *** uDmod2_minpack.lm::nlsLM_none ***
## minpack.lm nlsLM none i 5 summary statistics 0.0449 0.002 0.036 0.1169 time 0.03
## minpack.lm nlsLM none i 10 summary statistics 0.0427 0.0018 0.0333 0.1058 time 0.01
## minpack.lm nlsLM none i 15 summary statistics 0.0452 0.002 0.0363 0.118 time 0.05
## minpack.lm nlsLM none i 20 summary statistics 0.0427 0.0018 0.0333 0.1058 time 0.01
##
## ________________________________________________________________________________
## *** uDmod2_monmlp::monmlp.fit_BFGS ***
## monmlp monmlp.fit BFGS i 5 summary statistics 0.0547 0.003 0.0462 0.1159 time 0.21
## monmlp monmlp.fit BFGS i 10 summary statistics 0.0563 0.0032 0.0453 0.141 time 0.19
## monmlp monmlp.fit BFGS i 15 summary statistics 0.07 0.0049 0.0555 0.1829 time 0.21
## monmlp monmlp.fit BFGS i 20 summary statistics 0.0634 0.004 0.0511 0.1503 time 0.22
##
## ________________________________________________________________________________
## *** uDmod2_nlsr::nlxb_none ***
## 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
## nlsr nlxb none i 5 summary statistics 0.0786 0.0062 0.0615 0.2236 time 0.08
## 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
## nlsr nlxb none i 10 summary statistics 0.0427 0.0018 0.0331 0.1061 time 0.08
## 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
## nlsr nlxb none i 15 summary statistics 0.0427 0.0018 0.0333 0.1058 time 0.05
## 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
## nlsr nlxb none i 20 summary statistics 0.0514 0.0026 0.0416 0.1149 time 0.08
##
## ________________________________________________________________________________
## *** uDmod2_nnet::nnet_none ***
## nnet nnet none i 5 summary statistics 0.061 0.0037 0.049 0.1649 time 0
## nnet nnet none i 10 summary statistics 0.0722 0.0052 0.0551 0.1943 time 0.01
## nnet nnet none i 15 summary statistics 0.0602 0.0036 0.0486 0.1221 time 0.02
## nnet nnet none i 20 summary statistics 0.1231 0.0152 0.0947 0.3155 time 0
##
## ________________________________________________________________________________
## *** uDmod2_qrnn::qrnn.fit_none ***
## qrnn qrnn.fit none i 5 summary statistics 0.0863 0.0074 0.0584 0.2552 time 0.15
## qrnn qrnn.fit none i 10 summary statistics 0.1124 0.0126 0.0853 0.2858 time 0.28
## qrnn qrnn.fit none i 15 summary statistics 0.1024 0.0105 0.0781 0.3015 time 0.16
## qrnn qrnn.fit none i 20 summary statistics 0.1976 0.039 0.1402 0.5224 time 0.14
##
## ________________________________________________________________________________
## *** uDmod2_radiant.model::nn_none ***
## radiant.model nn none i 5 summary statistics 0.1458 0.0213 0.1115 0.423 time 0.02
## radiant.model nn none i 10 summary statistics 0.0779 0.0061 0.0604 0.2224 time 0.02
## radiant.model nn none i 15 summary statistics 0.0762 0.0058 0.0648 0.207 time 0.03
## radiant.model nn none i 20 summary statistics 0.0965 0.0093 0.0753 0.2577 time 0.04
##
## ________________________________________________________________________________
## *** uDmod2_rminer::fit_none ***
## rminer fit none i 5 summary statistics 0.0614 0.0038 0.0482 0.1392 time 0.02
## rminer fit none i 10 summary statistics 0.0428 0.0018 0.033 0.1062 time 0.02
## rminer fit none i 15 summary statistics 0.0428 0.0018 0.0331 0.106 time 0.04
## rminer fit none i 20 summary statistics 0.0589 0.0035 0.0467 0.131 time 0.02
##
## ________________________________________________________________________________
## *** uDmod2_validann::ann_BFGS ***
## initial value 43.518902
## iter 20 value 10.334403
## iter 40 value 5.173817
## iter 60 value 3.967449
## iter 80 value 2.787361
## iter 100 value 2.349189
## iter 120 value 1.997217
## iter 140 value 1.929990
## iter 160 value 1.887473
## iter 180 value 1.840914
## iter 200 value 1.814120
## final value 1.814120
## stopped after 200 iterations
## initial value 42.090638
## iter 20 value 11.811780
## iter 40 value 7.986993
## iter 60 value 2.053474
## iter 80 value 1.239201
## iter 100 value 0.987527
## iter 120 value 0.784896
## iter 140 value 0.756844
## iter 160 value 0.709562
## iter 180 value 0.672078
## iter 200 value 0.483128
## final value 0.483128
## stopped after 200 iterations
## initial value 46.989530
## iter 20 value 9.910176
## iter 40 value 3.403414
## iter 60 value 0.931478
## iter 80 value 0.791018
## iter 100 value 0.710153
## iter 120 value 0.704861
## iter 140 value 0.703019
## iter 160 value 0.700895
## iter 180 value 0.698491
## iter 200 value 0.692037
## final value 0.692037
## stopped after 200 iterations
## initial value 47.043666
## iter 20 value 18.302239
## iter 40 value 7.726850
## iter 60 value 1.017121
## iter 80 value 0.875459
## iter 100 value 0.716550
## iter 120 value 0.684195
## iter 140 value 0.681584
## iter 160 value 0.679605
## iter 180 value 0.679468
## iter 200 value 0.679178
## final value 0.679178
## stopped after 200 iterations
## initial value 83.166566
## iter 20 value 16.476781
## iter 40 value 6.824058
## iter 60 value 2.512412
## iter 80 value 1.434484
## iter 100 value 1.285903
## iter 120 value 0.864023
## iter 140 value 0.788956
## iter 160 value 0.787246
## iter 180 value 0.787201
## final value 0.787190
## converged
## validann ann BFGS i 5 summary statistics 0.0649 0.0042 0.0529 0.139 time 0.58
## initial value 52.651567
## iter 20 value 11.523479
## iter 40 value 2.712586
## iter 60 value 1.279187
## iter 80 value 1.172521
## iter 100 value 1.101528
## iter 120 value 1.014816
## iter 140 value 0.905138
## iter 160 value 0.857306
## iter 180 value 0.829568
## iter 200 value 0.815940
## final value 0.815940
## stopped after 200 iterations
## initial value 51.446859
## iter 20 value 6.601526
## iter 40 value 1.823475
## iter 60 value 1.337343
## iter 80 value 1.042729
## iter 100 value 0.981196
## iter 120 value 0.969322
## iter 140 value 0.959248
## iter 160 value 0.956273
## iter 180 value 0.953239
## iter 200 value 0.950041
## final value 0.950041
## stopped after 200 iterations
## initial value 50.169389
## iter 20 value 16.544802
## iter 40 value 4.752630
## iter 60 value 1.636213
## iter 80 value 1.076097
## iter 100 value 0.805353
## iter 120 value 0.680602
## iter 140 value 0.615184
## iter 160 value 0.586666
## iter 180 value 0.566147
## iter 200 value 0.546984
## final value 0.546984
## stopped after 200 iterations
## initial value 63.693214
## iter 20 value 15.809074
## iter 40 value 2.853051
## iter 60 value 1.511309
## iter 80 value 1.153641
## iter 100 value 0.810766
## iter 120 value 0.660896
## iter 140 value 0.517316
## iter 160 value 0.459112
## iter 180 value 0.412508
## iter 200 value 0.371258
## final value 0.371258
## stopped after 200 iterations
## initial value 74.362378
## iter 20 value 14.139272
## iter 40 value 3.466589
## iter 60 value 1.606173
## iter 80 value 1.220199
## iter 100 value 0.849215
## iter 120 value 0.613318
## iter 140 value 0.515366
## iter 160 value 0.440637
## iter 180 value 0.398780
## iter 200 value 0.363807
## final value 0.363807
## stopped after 200 iterations
## validann ann BFGS i 10 summary statistics 0.0441 0.0019 0.0342 0.1055 time 0.6
## initial value 59.771864
## iter 20 value 6.772142
## iter 40 value 2.652942
## iter 60 value 0.810245
## iter 80 value 0.518870
## iter 100 value 0.364918
## iter 120 value 0.344420
## iter 140 value 0.341244
## iter 160 value 0.341097
## iter 180 value 0.340897
## iter 200 value 0.340762
## final value 0.340762
## stopped after 200 iterations
## initial value 54.848849
## iter 20 value 14.814672
## iter 40 value 2.151350
## iter 60 value 1.158601
## iter 80 value 1.124651
## iter 100 value 1.105640
## iter 120 value 1.103333
## iter 140 value 1.099380
## iter 160 value 1.097662
## iter 180 value 1.097046
## iter 200 value 1.095899
## final value 1.095899
## stopped after 200 iterations
## initial value 53.154753
## iter 20 value 14.346768
## iter 40 value 4.853147
## iter 60 value 0.680981
## iter 80 value 0.409153
## iter 100 value 0.345841
## iter 120 value 0.342393
## iter 140 value 0.341821
## iter 160 value 0.341570
## iter 180 value 0.341490
## iter 200 value 0.341432
## final value 0.341432
## stopped after 200 iterations
## initial value 57.069867
## iter 20 value 18.947142
## iter 40 value 6.087897
## iter 60 value 3.206982
## iter 80 value 3.117891
## iter 100 value 2.963262
## iter 120 value 2.900658
## iter 140 value 2.872228
## iter 160 value 2.850273
## iter 180 value 2.842630
## iter 200 value 2.835726
## final value 2.835726
## stopped after 200 iterations
## initial value 54.258200
## iter 20 value 7.110608
## iter 40 value 5.349539
## iter 60 value 2.704134
## iter 80 value 2.176618
## iter 100 value 1.276113
## iter 120 value 0.863686
## iter 140 value 0.775675
## iter 160 value 0.715350
## iter 180 value 0.708819
## iter 200 value 0.690135
## final value 0.690135
## stopped after 200 iterations
## validann ann BFGS i 15 summary statistics 0.0608 0.0037 0.0485 0.1295 time 0.59
## initial value 63.112247
## iter 20 value 11.082102
## iter 40 value 7.474100
## iter 60 value 3.185472
## iter 80 value 3.090226
## iter 100 value 2.963862
## iter 120 value 2.906785
## iter 140 value 2.891564
## iter 160 value 2.844135
## iter 180 value 2.838990
## iter 200 value 2.830444
## final value 2.830444
## stopped after 200 iterations
## initial value 56.176995
## iter 20 value 13.021712
## iter 40 value 3.109237
## iter 60 value 0.779883
## iter 80 value 0.708562
## iter 100 value 0.688952
## iter 120 value 0.685023
## iter 140 value 0.682037
## iter 160 value 0.681044
## iter 180 value 0.679488
## iter 200 value 0.679244
## final value 0.679244
## stopped after 200 iterations
## initial value 56.169305
## iter 20 value 7.970442
## iter 40 value 2.301301
## iter 60 value 0.525062
## iter 80 value 0.401599
## iter 100 value 0.312907
## iter 120 value 0.309725
## iter 140 value 0.309063
## iter 160 value 0.307285
## iter 180 value 0.305584
## iter 200 value 0.302893
## final value 0.302893
## stopped after 200 iterations
## initial value 65.393887
## iter 20 value 13.319620
## iter 40 value 2.899634
## iter 60 value 1.615655
## iter 80 value 1.460861
## iter 100 value 1.246407
## iter 120 value 1.082784
## iter 140 value 1.042137
## iter 160 value 1.027638
## iter 180 value 1.014964
## iter 200 value 1.006311
## final value 1.006311
## stopped after 200 iterations
## initial value 55.495834
## iter 20 value 15.547751
## iter 40 value 9.858420
## iter 60 value 5.264401
## iter 80 value 2.151665
## iter 100 value 1.330268
## iter 120 value 1.091248
## iter 140 value 0.935632
## iter 160 value 0.844494
## iter 180 value 0.812381
## iter 200 value 0.732464
## final value 0.732464
## stopped after 200 iterations
## validann ann BFGS i 20 summary statistics 0.0626 0.0039 0.0518 0.1596 time 0.58
##
## ________________________________________________________________________________
## *** uDmod2_validann::ann_L-BFGS-B ***
## iter 20 value 16.408811
## iter 40 value 8.341365
## iter 60 value 2.721697
## iter 80 value 1.579768
## iter 100 value 1.381116
## iter 120 value 1.226274
## iter 140 value 1.189820
## iter 160 value 1.046699
## iter 180 value 0.899385
## iter 200 value 0.804997
## final value 0.804488
## stopped after 201 iterations
## iter 20 value 16.537337
## iter 40 value 5.025521
## iter 60 value 1.768610
## iter 80 value 1.317662
## iter 100 value 0.993689
## iter 120 value 0.721411
## iter 140 value 0.593814
## iter 160 value 0.561029
## iter 180 value 0.527975
## iter 200 value 0.491355
## final value 0.490407
## stopped after 201 iterations
## iter 20 value 17.769969
## iter 40 value 7.896056
## iter 60 value 4.375048
## iter 80 value 2.121632
## iter 100 value 1.614820
## iter 120 value 1.546494
## iter 140 value 1.492732
## iter 160 value 1.418384
## iter 180 value 1.329643
## iter 200 value 1.272157
## final value 1.270129
## stopped after 201 iterations
## iter 20 value 19.573025
## iter 40 value 9.158959
## iter 60 value 6.186805
## iter 80 value 4.473666
## iter 100 value 3.916632
## iter 120 value 3.539764
## iter 140 value 2.684982
## iter 160 value 1.732527
## iter 180 value 1.025748
## iter 200 value 0.683639
## final value 0.672612
## stopped after 201 iterations
## iter 20 value 18.321631
## iter 40 value 10.532651
## iter 60 value 8.311398
## iter 80 value 7.822429
## iter 100 value 4.907774
## iter 120 value 2.518234
## iter 140 value 1.987785
## iter 160 value 1.434332
## iter 180 value 1.279026
## iter 200 value 1.211882
## final value 1.211462
## stopped after 201 iterations
## validann ann L-BFGS-B i 5 summary statistics 0.0806 0.0065 0.0664 0.1896 time 0.67
## iter 20 value 15.564768
## iter 40 value 4.393879
## iter 60 value 2.013254
## iter 80 value 1.419406
## iter 100 value 1.351036
## iter 120 value 1.276818
## iter 140 value 1.253177
## iter 160 value 1.171888
## iter 180 value 1.028339
## iter 200 value 1.001496
## final value 1.001009
## stopped after 201 iterations
## iter 20 value 18.092265
## iter 40 value 14.249638
## iter 60 value 12.626558
## iter 80 value 11.222080
## iter 100 value 8.907638
## iter 120 value 8.661566
## iter 140 value 8.613019
## iter 160 value 8.519389
## iter 180 value 8.091297
## iter 200 value 7.243578
## final value 7.115705
## stopped after 201 iterations
## iter 20 value 13.872168
## iter 40 value 3.739694
## iter 60 value 3.009793
## iter 80 value 2.432805
## iter 100 value 2.000963
## iter 120 value 1.737324
## iter 140 value 1.625901
## iter 160 value 1.417048
## iter 180 value 1.147510
## iter 200 value 0.951248
## final value 0.949443
## stopped after 201 iterations
## iter 20 value 12.512675
## iter 40 value 5.910515
## iter 60 value 5.340765
## iter 80 value 5.021799
## iter 100 value 4.951375
## iter 120 value 4.923113
## iter 140 value 4.897364
## iter 160 value 4.836097
## iter 180 value 4.739171
## iter 200 value 4.693401
## final value 4.691668
## stopped after 201 iterations
## iter 20 value 19.205489
## iter 40 value 12.566956
## iter 60 value 2.741756
## iter 80 value 1.622560
## iter 100 value 1.495163
## iter 120 value 1.460699
## iter 140 value 1.417301
## iter 160 value 1.348031
## iter 180 value 1.331440
## iter 200 value 1.324241
## final value 1.324015
## stopped after 201 iterations
## validann ann L-BFGS-B i 10 summary statistics 0.0842 0.0071 0.069 0.2106 time 0.67
## iter 20 value 18.149398
## iter 40 value 5.547339
## iter 60 value 2.403143
## iter 80 value 1.452239
## iter 100 value 1.428888
## iter 120 value 1.404731
## iter 140 value 1.284875
## iter 160 value 1.212449
## iter 180 value 1.185045
## iter 200 value 1.175941
## final value 1.174506
## stopped after 201 iterations
## iter 20 value 19.282947
## iter 40 value 16.888280
## iter 60 value 14.233165
## iter 80 value 13.964944
## iter 100 value 11.266994
## iter 120 value 9.467277
## iter 140 value 8.498950
## iter 160 value 7.632026
## iter 180 value 5.713825
## iter 200 value 4.281477
## final value 4.243758
## stopped after 201 iterations
## iter 20 value 16.514550
## iter 40 value 7.207887
## iter 60 value 5.165891
## iter 80 value 4.749011
## iter 100 value 2.550998
## iter 120 value 1.812965
## iter 140 value 1.681012
## iter 160 value 1.565516
## iter 180 value 1.188604
## iter 200 value 1.128396
## final value 1.124730
## stopped after 201 iterations
## iter 20 value 18.740875
## iter 40 value 10.751769
## iter 60 value 8.075753
## iter 80 value 5.084224
## iter 100 value 4.700890
## iter 120 value 4.335013
## iter 140 value 4.188504
## iter 160 value 3.933609
## iter 180 value 3.387972
## iter 200 value 3.229966
## final value 3.222592
## stopped after 201 iterations
## iter 20 value 17.170491
## iter 40 value 7.317873
## iter 60 value 4.589318
## iter 80 value 1.820046
## iter 100 value 1.396533
## iter 120 value 1.092356
## iter 140 value 0.924647
## iter 160 value 0.913239
## iter 180 value 0.868343
## iter 200 value 0.843988
## final value 0.843615
## stopped after 201 iterations
## validann ann L-BFGS-B i 15 summary statistics 0.0672 0.0045 0.0547 0.1307 time 0.66
## iter 20 value 18.828025
## iter 40 value 6.470903
## iter 60 value 3.575840
## iter 80 value 2.322933
## iter 100 value 2.127615
## iter 120 value 1.972462
## iter 140 value 1.933212
## iter 160 value 1.900189
## iter 180 value 1.735909
## iter 200 value 1.664089
## final value 1.656771
## stopped after 201 iterations
## iter 20 value 15.939808
## iter 40 value 5.836341
## iter 60 value 3.822687
## iter 80 value 2.581847
## iter 100 value 1.707872
## iter 120 value 1.492480
## iter 140 value 1.375164
## iter 160 value 1.326590
## iter 180 value 1.239771
## iter 200 value 1.173961
## final value 1.173413
## stopped after 201 iterations
## iter 20 value 19.117268
## iter 40 value 6.604334
## iter 60 value 2.058685
## iter 80 value 1.685593
## iter 100 value 1.348489
## iter 120 value 1.290179
## iter 140 value 1.139024
## iter 160 value 1.007550
## iter 180 value 0.956703
## iter 200 value 0.922415
## final value 0.921975
## stopped after 201 iterations
## iter 20 value 17.965985
## iter 40 value 13.177635
## iter 60 value 10.950545
## iter 80 value 10.025083
## iter 100 value 6.212743
## iter 120 value 2.858531
## iter 140 value 1.966893
## iter 160 value 1.194316
## iter 180 value 0.939703
## iter 200 value 0.817744
## final value 0.815968
## stopped after 201 iterations
## iter 20 value 16.559040
## iter 40 value 3.892537
## iter 60 value 1.611380
## iter 80 value 1.413649
## iter 100 value 1.312492
## iter 120 value 1.280924
## iter 140 value 1.233686
## iter 160 value 1.206360
## iter 180 value 1.170720
## iter 200 value 1.155827
## final value 1.154829
## stopped after 201 iterations
## validann ann L-BFGS-B i 20 summary statistics 0.0787 0.0062 0.06 0.1956 time 0.64
##
## ________________________________________________________________________________
## *** uDreyfus1_brnn::brnn_gaussNewton ***
## Number of parameters (weights and biases) to estimate: 9
## Nguyen-Widrow method
## Scaling factor= 2.1
## gamma= 8.9918 alpha= 0.0622 beta= 202955.4
## Number of parameters (weights and biases) to estimate: 9
## Nguyen-Widrow method
## Scaling factor= 2.1
## gamma= 8.9834 alpha= 0.0638 beta= 5805.409
## Number of parameters (weights and biases) to estimate: 9
## Nguyen-Widrow method
## Scaling factor= 2.1
## gamma= 8.9649 alpha= 0.0638 beta= 36320.81
## Number of parameters (weights and biases) to estimate: 9
## Nguyen-Widrow method
## Scaling factor= 2.1
## gamma= 8.9816 alpha= 0.0639 beta= 5601.841
## Number of parameters (weights and biases) to estimate: 9
## Nguyen-Widrow method
## Scaling factor= 2.1
## gamma= 8.9891 alpha= 0.0633 beta= 6509.595
## brnn brnn gaussNewton i 5 summary statistics 0.013 0.0002 0.01 0.0288 time 0.01
## Number of parameters (weights and biases) to estimate: 9
## Nguyen-Widrow method
## Scaling factor= 2.1
## gamma= 8.9888 alpha= 0.063 beta= 12253.28
## Number of parameters (weights and biases) to estimate: 9
## Nguyen-Widrow method
## Scaling factor= 2.1
## gamma= 8.99 alpha= 0.0626 beta= 51813.69
## Number of parameters (weights and biases) to estimate: 9
## Nguyen-Widrow method
## Scaling factor= 2.1
## gamma= 8.9815 alpha= 0.064 beta= 4903.016
## Number of parameters (weights and biases) to estimate: 9
## Nguyen-Widrow method
## Scaling factor= 2.1
## gamma= 8.9754 alpha= 0.0645 beta= 4204.675
## Number of parameters (weights and biases) to estimate: 9
## Nguyen-Widrow method
## Scaling factor= 2.1
## gamma= 8.9771 alpha= 0.0631 beta= 54687.18
## brnn brnn gaussNewton i 10 summary statistics 0.0045 0 0.0034 0.0163 time 0
## Number of parameters (weights and biases) to estimate: 9
## Nguyen-Widrow method
## Scaling factor= 2.1
## gamma= 8.9917 alpha= 0.0622 beta= 58770.53
## Number of parameters (weights and biases) to estimate: 9
## Nguyen-Widrow method
## Scaling factor= 2.1
## gamma= 8.9921 alpha= 0.0626 beta= 30679.68
## Number of parameters (weights and biases) to estimate: 9
## Nguyen-Widrow method
## Scaling factor= 2.1
## gamma= 8.9912 alpha= 0.0624 beta= 87146.81
## Number of parameters (weights and biases) to estimate: 9
## Nguyen-Widrow method
## Scaling factor= 2.1
## gamma= 8.9864 alpha= 0.0636 beta= 5346.364
## Number of parameters (weights and biases) to estimate: 9
## Nguyen-Widrow method
## Scaling factor= 2.1
## gamma= 8.9727 alpha= 0.0634 beta= 40810.38
## brnn brnn gaussNewton i 15 summary statistics 0.0052 0 0.0041 0.018 time 0
## Number of parameters (weights and biases) to estimate: 9
## Nguyen-Widrow method
## Scaling factor= 2.1
## gamma= 8.9712 alpha= 0.0719 beta= 2255.945
## Number of parameters (weights and biases) to estimate: 9
## Nguyen-Widrow method
## Scaling factor= 2.1
## gamma= 8.9911 alpha= 0.0624 beta= 97926.63
## Number of parameters (weights and biases) to estimate: 9
## Nguyen-Widrow method
## Scaling factor= 2.1
## gamma= 8.9698 alpha= 0.0653 beta= 2137.427
## Number of parameters (weights and biases) to estimate: 9
## Nguyen-Widrow method
## Scaling factor= 2.1
## gamma= 8.9845 alpha= 0.0638 beta= 5351.836
## Number of parameters (weights and biases) to estimate: 9
## Nguyen-Widrow method
## Scaling factor= 2.1
## gamma= 8.9775 alpha= 0.0631 beta= 62155.77
## brnn brnn gaussNewton i 20 summary statistics 0.0042 0 0.003 0.0152 time 0
##
## ________________________________________________________________________________
## *** uDreyfus1_CaDENCE::cadence.fit_optim ***
## n.hidden = 3 --> 1 * NLL = -138.8106 ; penalty = 0; BIC = -222.5757 ; AICc = -237.9546 ; AIC = -249.6212
## n.hidden = 3 --> 1 * NLL = -152.5409 ; penalty = 0; BIC = -250.0362 ; AICc = -265.4151 ; AIC = -277.0818
## n.hidden = 3 --> 1 * NLL = -156.4792 ; penalty = 0; BIC = -257.9128 ; AICc = -273.2917 ; AIC = -284.9583
## n.hidden = 3 --> 1 * NLL = -138.997 ; penalty = 0; BIC = -222.9485 ; AICc = -238.3274 ; AIC = -249.9941
## n.hidden = 3 --> 1 * NLL = -175.3616 ; penalty = 0; BIC = -295.6777 ; AICc = -311.0566 ; AIC = -322.7232
## CaDENCE cadence.fit optim i 5 summary statistics 0.0685 0.0047 0.0343 0.2265 time 0.86
## n.hidden = 3 --> 1 * NLL = -142.6312 ; penalty = 0; BIC = -230.2168 ; AICc = -245.5956 ; AIC = -257.2623
## n.hidden = 3 --> 1 * NLL = -105.2119 ; penalty = 0; BIC = -155.3783 ; AICc = -170.7572 ; AIC = -182.4239
## n.hidden = 3 --> 1 * NLL = -273.6984 ; penalty = 0; BIC = -492.3512 ; AICc = -507.7301 ; AIC = -519.3968
## n.hidden = 3 --> 1 * NLL = -122.8041 ; penalty = 0; BIC = -190.5627 ; AICc = -205.9416 ; AIC = -217.6083
## n.hidden = 3 --> 1 * NLL = -134.5294 ; penalty = 0; BIC = -214.0133 ; AICc = -229.3922 ; AIC = -241.0588
## CaDENCE cadence.fit optim i 10 summary statistics 1.1708 1.3708 0.6597 2.5653 time 0.97
## n.hidden = 3 --> 1 * NLL = -133.2773 ; penalty = 0; BIC = -211.5091 ; AICc = -226.888 ; AIC = -238.5547
## n.hidden = 3 --> 1 * NLL = -197.7411 ; penalty = 0; BIC = -340.4367 ; AICc = -355.8156 ; AIC = -367.4823
## n.hidden = 3 --> 1 * NLL = -135.0141 ; penalty = 0; BIC = -214.9827 ; AICc = -230.3616 ; AIC = -242.0283
## n.hidden = 3 --> 1 * NLL = -147.0479 ; penalty = 0; BIC = -239.0503 ; AICc = -254.4291 ; AIC = -266.0958
## n.hidden = 3 --> 1 * NLL = -162.0521 ; penalty = 0; BIC = -269.0587 ; AICc = -284.4376 ; AIC = -296.1043
## CaDENCE cadence.fit optim i 15 summary statistics 0.3776 0.1426 0.1927 1.2467 time 0.81
## n.hidden = 3 --> 1 * NLL = -251.1959 ; penalty = 0; BIC = -447.3462 ; AICc = -462.725 ; AIC = -474.3917
## n.hidden = 3 --> 1 * NLL = -173.4832 ; penalty = 0; BIC = -291.9209 ; AICc = -307.2998 ; AIC = -318.9665
## n.hidden = 3 --> 1 * NLL = -152.2895 ; penalty = 0; BIC = -249.5335 ; AICc = -264.9124 ; AIC = -276.5791
## n.hidden = 3 --> 1 * NLL = -138.8479 ; penalty = 0; BIC = -222.6501 ; AICc = -238.029 ; AIC = -249.6957
## n.hidden = 3 --> 1 * NLL = -207.3887 ; penalty = 0; BIC = -359.7319 ; AICc = -375.1108 ; AIC = -386.7775
## CaDENCE cadence.fit optim i 20 summary statistics 0.0669 0.0045 0.0334 0.2611 time 0.69
##
## ________________________________________________________________________________
## *** uDreyfus1_MachineShop::fit_none ***
## MachineShop fit none i 5 summary statistics 0.0034 0 0.0028 0.0094 time 0.01
## MachineShop fit none i 10 summary statistics 0.0024 0 0.002 0.0067 time 0
## MachineShop fit none i 15 summary statistics 0.0764 0.0058 0.0571 0.2265 time 0
## MachineShop fit none i 20 summary statistics 0.0023 0 0.0019 0.0062 time 0
##
## ________________________________________________________________________________
## *** uDreyfus1_minpack.lm::nlsLM_none ***
## minpack.lm nlsLM none i 5 summary statistics 0 0 0 0.0001 time 0
## minpack.lm nlsLM none i 10 summary statistics 0 0 0 0.0001 time 0.02
## minpack.lm nlsLM none i 15 summary statistics 0 0 0 0.0001 time 0
## minpack.lm nlsLM none i 20 summary statistics 0.1132 0.0128 0.0799 0.3291 time 0
##
## ________________________________________________________________________________
## *** uDreyfus1_monmlp::monmlp.fit_BFGS ***
## monmlp monmlp.fit BFGS i 5 summary statistics 0.0166 0.0003 0.0134 0.0455 time 0.19
## monmlp monmlp.fit BFGS i 10 summary statistics 0.0537 0.0029 0.0438 0.136 time 0.18
## monmlp monmlp.fit BFGS i 15 summary statistics 0.0361 0.0013 0.029 0.0988 time 0.19
## monmlp monmlp.fit BFGS i 20 summary statistics 0.0477 0.0023 0.0385 0.1325 time 0.2
##
## ________________________________________________________________________________
## *** uDreyfus1_nlsr::nlxb_none ***
## 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
## nlsr nlxb none i 5 summary statistics 0 0 0 0.0001 time 0.01
## 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
## nlsr nlxb none i 10 summary statistics 0.0932 0.0087 0.0737 0.2452 time 0.05
## 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
## nlsr nlxb none i 15 summary statistics 0 0 0 0.0001 time 0.01
## 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
## nlsr nlxb none i 20 summary statistics 0 0 0 0.0001 time 0.02
##
## ________________________________________________________________________________
## *** uDreyfus1_nnet::nnet_none ***
## nnet nnet none i 5 summary statistics 0.0022 0 0.0018 0.0047 time 0
## nnet nnet none i 10 summary statistics 0.0026 0 0.0021 0.0071 time 0
## nnet nnet none i 15 summary statistics 0.0966 0.0093 0.0799 0.2419 time 0
## nnet nnet none i 20 summary statistics 0.0509 0.0026 0.0422 0.1151 time 0
##
## ________________________________________________________________________________
## *** uDreyfus1_qrnn::qrnn.fit_none ***
## qrnn qrnn.fit none i 5 summary statistics 0.2779 0.0772 0.1792 0.8503 time 0.21
## qrnn qrnn.fit none i 10 summary statistics 0.3167 0.1003 0.1981 0.9742 time 0.08
## qrnn qrnn.fit none i 15 summary statistics 0.0541 0.0029 0.0277 0.203 time 0.19
## qrnn qrnn.fit none i 20 summary statistics 0.3175 0.1008 0.1985 0.9765 time 0.08
##
## ________________________________________________________________________________
## *** uDreyfus1_radiant.model::nn_none ***
## radiant.model nn none i 5 summary statistics 0.0798 0.0064 0.0601 0.2331 time 0.01
## radiant.model nn none i 10 summary statistics 0.0055 0 0.0042 0.0116 time 0.03
## radiant.model nn none i 15 summary statistics 0.0721 0.0052 0.0528 0.2167 time 0.01
## radiant.model nn none i 20 summary statistics 0.1108 0.0123 0.0841 0.3252 time 0.03
##
## ________________________________________________________________________________
## *** uDreyfus1_rminer::fit_none ***
## rminer fit none i 5 summary statistics 0.002 0 0.0017 0.005 time 0
## rminer fit none i 10 summary statistics 0.003 0 0.0023 0.0095 time 0.01
## rminer fit none i 15 summary statistics 0.0022 0 0.0018 0.0056 time 0
## rminer fit none i 20 summary statistics 0.0033 0 0.0027 0.01 time 0.02
##
## ________________________________________________________________________________
## *** uDreyfus1_validann::ann_BFGS ***
## initial value 57.414077
## iter 20 value 0.736004
## iter 40 value 0.159775
## iter 60 value 0.157782
## iter 80 value 0.157590
## iter 100 value 0.157153
## iter 120 value 0.157089
## iter 140 value 0.157011
## final value 0.157010
## converged
## initial value 69.541632
## iter 20 value 0.721297
## iter 40 value 0.016932
## iter 60 value 0.000631
## iter 80 value 0.000265
## iter 100 value 0.000252
## iter 120 value 0.000220
## iter 140 value 0.000179
## iter 160 value 0.000135
## final value 0.000099
## converged
## initial value 52.025349
## iter 20 value 0.336196
## iter 40 value 0.004815
## final value 0.000082
## converged
## initial value 59.115127
## iter 20 value 2.138257
## iter 40 value 0.253148
## iter 60 value 0.037925
## iter 80 value 0.005529
## iter 100 value 0.000576
## iter 120 value 0.000436
## iter 140 value 0.000434
## iter 160 value 0.000427
## final value 0.000427
## converged
## initial value 52.087283
## iter 20 value 1.987353
## iter 40 value 0.023471
## iter 60 value 0.000462
## final value 0.000072
## converged
## validann ann BFGS i 5 summary statistics 0.0019 0 0.0016 0.006 time 0.14
## initial value 43.344008
## iter 20 value 1.998967
## iter 40 value 0.187750
## iter 60 value 0.115414
## iter 80 value 0.047680
## iter 100 value 0.000100
## iter 100 value 0.000098
## iter 100 value 0.000098
## final value 0.000098
## converged
## initial value 59.687069
## iter 20 value 1.922854
## iter 40 value 0.374548
## iter 60 value 0.115533
## iter 80 value 0.051687
## iter 100 value 0.015576
## iter 120 value 0.002690
## iter 140 value 0.000244
## iter 160 value 0.000174
## iter 180 value 0.000132
## final value 0.000091
## converged
## initial value 39.768622
## iter 20 value 2.068975
## iter 40 value 0.255278
## iter 60 value 0.051382
## iter 80 value 0.006641
## iter 100 value 0.000123
## final value 0.000096
## converged
## initial value 42.093512
## iter 20 value 1.559761
## iter 40 value 0.026184
## final value 0.000039
## converged
## initial value 58.643112
## iter 20 value 3.093504
## iter 40 value 2.120536
## iter 60 value 0.004836
## iter 80 value 0.000611
## iter 100 value 0.000278
## iter 120 value 0.000248
## iter 140 value 0.000216
## iter 160 value 0.000186
## iter 180 value 0.000138
## final value 0.000099
## converged
## validann ann BFGS i 10 summary statistics 0.0023 0 0.0019 0.0059 time 0.36
## initial value 66.086208
## iter 20 value 0.704790
## iter 40 value 0.148883
## iter 60 value 0.036111
## iter 80 value 0.000840
## final value 0.000083
## converged
## initial value 49.908775
## iter 20 value 1.197727
## iter 40 value 0.255928
## iter 60 value 0.255752
## iter 80 value 0.255712
## iter 100 value 0.255703
## iter 120 value 0.255673
## iter 140 value 0.255651
## iter 160 value 0.165550
## iter 180 value 0.109120
## iter 200 value 0.101225
## final value 0.101225
## stopped after 200 iterations
## initial value 63.511823
## iter 20 value 0.665519
## iter 40 value 0.103982
## iter 60 value 0.098985
## iter 80 value 0.098962
## iter 100 value 0.098848
## iter 120 value 0.098845
## iter 140 value 0.098841
## iter 160 value 0.098839
## iter 180 value 0.098836
## final value 0.098835
## converged
## initial value 44.440699
## iter 20 value 2.178690
## iter 40 value 0.296739
## iter 60 value 0.253083
## iter 80 value 0.250880
## iter 100 value 0.248279
## iter 120 value 0.247520
## iter 140 value 0.247001
## iter 160 value 0.246916
## iter 180 value 0.246798
## final value 0.246787
## converged
## initial value 25.915951
## iter 20 value 0.787717
## iter 40 value 0.100339
## iter 60 value 0.090032
## iter 80 value 0.015152
## iter 100 value 0.000812
## iter 120 value 0.000302
## iter 140 value 0.000278
## iter 160 value 0.000246
## iter 180 value 0.000226
## iter 200 value 0.000182
## final value 0.000182
## stopped after 200 iterations
## validann ann BFGS i 15 summary statistics 0.0031 0 0.0025 0.0088 time 0.36
## initial value 64.088011
## iter 20 value 2.115633
## iter 40 value 0.208244
## iter 60 value 0.157028
## iter 80 value 0.157011
## final value 0.157010
## converged
## initial value 66.093031
## iter 20 value 1.775343
## iter 40 value 1.379413
## iter 60 value 1.154029
## iter 80 value 1.107416
## iter 100 value 1.037559
## iter 120 value 1.027376
## iter 140 value 0.989234
## iter 160 value 0.973645
## iter 180 value 0.970839
## iter 200 value 0.969591
## final value 0.969591
## stopped after 200 iterations
## initial value 58.598760
## iter 20 value 1.428098
## iter 40 value 0.486602
## iter 60 value 0.133800
## iter 80 value 0.111212
## iter 100 value 0.110890
## iter 120 value 0.109186
## iter 140 value 0.103247
## iter 160 value 0.097632
## iter 180 value 0.032047
## iter 200 value 0.003882
## final value 0.003882
## stopped after 200 iterations
## initial value 60.252429
## iter 20 value 2.231085
## iter 40 value 0.015976
## iter 60 value 0.000448
## iter 80 value 0.000228
## iter 100 value 0.000204
## iter 120 value 0.000146
## iter 140 value 0.000113
## final value 0.000092
## converged
## initial value 44.727180
## iter 20 value 2.244243
## iter 40 value 0.824970
## iter 60 value 0.373786
## iter 80 value 0.142360
## iter 100 value 0.012452
## iter 120 value 0.000432
## final value 0.000056
## converged
## validann ann BFGS i 20 summary statistics 0.0017 0 0.0014 0.0048 time 0.23
##
## ________________________________________________________________________________
## *** uDreyfus1_validann::ann_L-BFGS-B ***
## iter 20 value 1.213317
## iter 40 value 0.094904
## iter 60 value 0.037906
## iter 80 value 0.025308
## iter 100 value 0.020993
## iter 120 value 0.012017
## iter 140 value 0.008849
## iter 160 value 0.007915
## iter 180 value 0.006646
## iter 200 value 0.006466
## final value 0.006464
## stopped after 201 iterations
## iter 20 value 1.878801
## iter 40 value 0.155178
## iter 60 value 0.043207
## iter 80 value 0.023517
## iter 100 value 0.019131
## iter 120 value 0.011956
## iter 140 value 0.008781
## iter 160 value 0.008025
## iter 180 value 0.004378
## iter 200 value 0.001810
## final value 0.001689
## stopped after 201 iterations
## iter 20 value 2.228084
## iter 40 value 0.307061
## iter 60 value 0.276497
## iter 80 value 0.204641
## iter 100 value 0.103295
## iter 120 value 0.102987
## iter 140 value 0.102120
## iter 160 value 0.099589
## iter 180 value 0.098997
## iter 200 value 0.098965
## final value 0.098959
## stopped after 201 iterations
## iter 20 value 2.369983
## iter 40 value 1.766426
## iter 60 value 0.414238
## iter 80 value 0.170504
## iter 100 value 0.124233
## iter 120 value 0.121603
## iter 140 value 0.116591
## iter 160 value 0.111006
## iter 180 value 0.110546
## iter 200 value 0.110284
## final value 0.110275
## stopped after 201 iterations
## iter 20 value 1.432263
## iter 40 value 0.230412
## iter 60 value 0.071462
## iter 80 value 0.047650
## iter 100 value 0.023555
## iter 120 value 0.001744
## iter 140 value 0.001036
## iter 160 value 0.000303
## iter 180 value 0.000226
## iter 200 value 0.000155
## final value 0.000154
## stopped after 201 iterations
## validann ann L-BFGS-B i 5 summary statistics 0.0028 0 0.0019 0.0087 time 0.39
## iter 20 value 2.240251
## iter 40 value 0.562352
## iter 60 value 0.283740
## iter 80 value 0.250983
## iter 100 value 0.241408
## iter 120 value 0.208504
## iter 140 value 0.170947
## iter 160 value 0.169404
## iter 180 value 0.153764
## iter 200 value 0.085354
## final value 0.080033
## stopped after 201 iterations
## iter 20 value 2.326788
## iter 40 value 0.322828
## iter 60 value 0.269247
## iter 80 value 0.263196
## iter 100 value 0.254846
## iter 120 value 0.204938
## iter 140 value 0.176022
## iter 160 value 0.171649
## iter 180 value 0.163072
## iter 200 value 0.161827
## final value 0.161808
## stopped after 201 iterations
## iter 20 value 1.445541
## iter 40 value 0.257896
## iter 60 value 0.113764
## iter 80 value 0.110600
## iter 100 value 0.107960
## iter 120 value 0.104334
## iter 140 value 0.103866
## iter 160 value 0.102634
## iter 180 value 0.101295
## iter 200 value 0.100548
## final value 0.100486
## stopped after 201 iterations
## iter 20 value 2.081905
## iter 40 value 0.190070
## iter 60 value 0.081894
## iter 80 value 0.055381
## iter 100 value 0.034217
## iter 120 value 0.020118
## iter 140 value 0.012196
## iter 160 value 0.010002
## iter 180 value 0.009452
## iter 200 value 0.007725
## final value 0.007695
## stopped after 201 iterations
## iter 20 value 2.113946
## iter 40 value 0.922708
## iter 60 value 0.596239
## iter 80 value 0.511968
## iter 100 value 0.457237
## iter 120 value 0.204545
## iter 140 value 0.127005
## iter 160 value 0.115848
## iter 180 value 0.108689
## iter 200 value 0.105238
## final value 0.104943
## stopped after 201 iterations
## validann ann L-BFGS-B i 10 summary statistics 0.0739 0.0055 0.0536 0.2247 time 0.4
## iter 20 value 1.918067
## iter 40 value 0.262531
## iter 60 value 0.253917
## iter 80 value 0.103245
## iter 100 value 0.100760
## iter 120 value 0.100607
## iter 140 value 0.100561
## iter 160 value 0.100549
## iter 180 value 0.100507
## iter 200 value 0.100431
## final value 0.100427
## stopped after 201 iterations
## iter 20 value 2.249774
## iter 40 value 0.354709
## iter 60 value 0.169019
## iter 80 value 0.156346
## iter 100 value 0.132224
## iter 120 value 0.095724
## iter 140 value 0.076859
## iter 160 value 0.053747
## iter 180 value 0.050796
## iter 200 value 0.049959
## final value 0.049706
## stopped after 201 iterations
## iter 20 value 1.511137
## iter 40 value 0.352459
## iter 60 value 0.135055
## iter 80 value 0.104055
## iter 100 value 0.100769
## iter 120 value 0.100270
## iter 140 value 0.099889
## iter 160 value 0.099646
## iter 180 value 0.099470
## iter 200 value 0.098930
## final value 0.098925
## stopped after 201 iterations
## iter 20 value 1.563909
## iter 40 value 0.535888
## iter 60 value 0.129384
## iter 80 value 0.115803
## iter 100 value 0.109079
## iter 120 value 0.108639
## iter 140 value 0.107308
## iter 160 value 0.104885
## iter 180 value 0.084454
## iter 200 value 0.056802
## final value 0.056479
## stopped after 201 iterations
## iter 20 value 2.208240
## iter 40 value 0.202892
## iter 60 value 0.129848
## iter 80 value 0.117919
## iter 100 value 0.117237
## iter 120 value 0.113207
## iter 140 value 0.110749
## iter 160 value 0.106506
## iter 180 value 0.103518
## iter 200 value 0.067733
## final value 0.066770
## stopped after 201 iterations
## validann ann L-BFGS-B i 15 summary statistics 0.0589 0.0035 0.0482 0.1579 time 0.4
## iter 20 value 1.650501
## iter 40 value 0.140420
## iter 60 value 0.108764
## iter 80 value 0.029932
## iter 100 value 0.004573
## iter 120 value 0.002400
## iter 140 value 0.000716
## iter 160 value 0.000200
## iter 180 value 0.000089
## iter 200 value 0.000069
## final value 0.000068
## stopped after 201 iterations
## iter 20 value 0.765129
## iter 40 value 0.107582
## iter 60 value 0.105491
## iter 80 value 0.104011
## iter 100 value 0.102395
## iter 120 value 0.100681
## iter 140 value 0.100050
## iter 160 value 0.099748
## iter 180 value 0.098905
## iter 200 value 0.096571
## final value 0.095812
## stopped after 201 iterations
## iter 20 value 2.371422
## iter 40 value 0.308432
## iter 60 value 0.290084
## iter 80 value 0.258603
## iter 100 value 0.255898
## iter 120 value 0.255700
## final value 0.255699
## converged
## iter 20 value 1.185071
## iter 40 value 0.293481
## iter 60 value 0.165061
## iter 80 value 0.156794
## iter 100 value 0.153336
## iter 120 value 0.149166
## iter 140 value 0.117501
## iter 160 value 0.098954
## iter 180 value 0.093460
## iter 200 value 0.092732
## final value 0.092645
## stopped after 201 iterations
## iter 20 value 1.753562
## iter 40 value 0.277660
## iter 60 value 0.269979
## iter 80 value 0.266043
## iter 100 value 0.256451
## iter 120 value 0.255855
## iter 140 value 0.255368
## iter 160 value 0.254201
## iter 180 value 0.253926
## iter 200 value 0.253773
## final value 0.253769
## stopped after 201 iterations
## validann ann L-BFGS-B i 20 summary statistics 0.1149 0.0132 0.0829 0.3287 time 0.38
##
## ________________________________________________________________________________
## *** uDreyfus2_brnn::brnn_gaussNewton ***
## 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
## brnn brnn gaussNewton i 5 summary statistics 0.0913 0.0083 0.073 0.2241 time 0
## 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
## brnn brnn gaussNewton i 10 summary statistics 0.0913 0.0083 0.073 0.2241 time 0.02
## 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
## brnn brnn gaussNewton i 15 summary statistics 0.0913 0.0083 0.073 0.2241 time 0
## 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
## brnn brnn gaussNewton i 20 summary statistics 0.0913 0.0083 0.073 0.2241 time 0
##
## ________________________________________________________________________________
## *** uDreyfus2_CaDENCE::cadence.fit_optim ***
## n.hidden = 3 --> 1 * NLL = -28.34668 ; penalty = 0; BIC = -1.647811 ; AICc = -17.0267 ; AIC = -28.69337
## n.hidden = 3 --> 1 * NLL = -46.44518 ; penalty = 0; BIC = -37.84481 ; AICc = -53.2237 ; AIC = -64.89037
## n.hidden = 3 --> 1 * NLL = -38.77873 ; penalty = 0; BIC = -22.5119 ; AICc = -37.89079 ; AIC = -49.55746
## n.hidden = 3 --> 1 * NLL = -75.2649 ; penalty = 0; BIC = -95.48424 ; AICc = -110.8631 ; AIC = -122.5298
## n.hidden = 3 --> 1 * NLL = -63.67376 ; penalty = 0; BIC = -72.30197 ; AICc = -87.68086 ; AIC = -99.34753
## CaDENCE cadence.fit optim i 5 summary statistics 0.1464 0.0214 0.1042 0.48 time 1.02
## n.hidden = 3 --> 1 * NLL = -58.4369 ; penalty = 0; BIC = -61.82825 ; AICc = -77.20714 ; AIC = -88.87381
## n.hidden = 3 --> 1 * NLL = -28.34717 ; penalty = 0; BIC = -1.648772 ; AICc = -17.02766 ; AIC = -28.69433
## n.hidden = 3 --> 1 * NLL = -59.65283 ; penalty = 0; BIC = -64.26011 ; AICc = -79.639 ; AIC = -91.30567
## n.hidden = 3 --> 1 * NLL = -44.79389 ; penalty = 0; BIC = -34.54223 ; AICc = -49.92112 ; AIC = -61.58779
## n.hidden = 3 --> 1 * NLL = -28.03924 ; penalty = 0; BIC = -1.032931 ; AICc = -16.41182 ; AIC = -28.07849
## CaDENCE cadence.fit optim i 10 summary statistics 0.438 0.1919 0.2914 1.227 time 0.99
## n.hidden = 3 --> 1 * NLL = -75.58851 ; penalty = 0; BIC = -96.13147 ; AICc = -111.5104 ; AIC = -123.177
## n.hidden = 3 --> 1 * NLL = -46.2995 ; penalty = 0; BIC = -37.55344 ; AICc = -52.93233 ; AIC = -64.599
## n.hidden = 3 --> 1 * NLL = -68.22349 ; penalty = 0; BIC = -81.40142 ; AICc = -96.78031 ; AIC = -108.447
## n.hidden = 3 --> 1 * NLL = -59.64872 ; penalty = 0; BIC = -64.25188 ; AICc = -79.63077 ; AIC = -91.29744
## n.hidden = 3 --> 1 * NLL = -67.10913 ; penalty = 0; BIC = -79.1727 ; AICc = -94.5516 ; AIC = -106.2183
## CaDENCE cadence.fit optim i 15 summary statistics 0.118 0.0139 0.0895 0.3552 time 1.53
## n.hidden = 3 --> 1 * NLL = -49.19354 ; penalty = 0; BIC = -43.34152 ; AICc = -58.72042 ; AIC = -70.38708
## n.hidden = 3 --> 1 * NLL = -28.34011 ; penalty = 0; BIC = -1.634669 ; AICc = -17.01356 ; AIC = -28.68023
## n.hidden = 3 --> 1 * NLL = -75.46153 ; penalty = 0; BIC = -95.8775 ; AICc = -111.2564 ; AIC = -122.9231
## n.hidden = 3 --> 1 * NLL = -60.33555 ; penalty = 0; BIC = -65.62554 ; AICc = -81.00443 ; AIC = -92.6711
## n.hidden = 3 --> 1 * NLL = -75.17526 ; penalty = 0; BIC = -95.30495 ; AICc = -110.6838 ; AIC = -122.3505
## CaDENCE cadence.fit optim i 20 summary statistics 0.0908 0.0082 0.0722 0.2249 time 1.5
##
## ________________________________________________________________________________
## *** uDreyfus2_MachineShop::fit_none ***
## MachineShop fit none i 5 summary statistics 0.2366 0.056 0.1822 0.6644 time 0.02
## MachineShop fit none i 10 summary statistics 0.1303 0.017 0.1013 0.2849 time 0
## MachineShop fit none i 15 summary statistics 0.0906 0.0082 0.0725 0.2205 time 0.01
## MachineShop fit none i 20 summary statistics 0.0906 0.0082 0.0724 0.2202 time 0
##
## ________________________________________________________________________________
## *** uDreyfus2_minpack.lm::nlsLM_none ***
## minpack.lm nlsLM none i 5 summary statistics 0.157 0.0246 0.1188 0.4688 time 0
## minpack.lm nlsLM none i 10 summary statistics 0.0906 0.0082 0.0723 0.2197 time 0.02
## minpack.lm nlsLM none i 15 summary statistics 0.1123 0.0126 0.0901 0.2734 time 0.02
## minpack.lm nlsLM none i 20 summary statistics 0.0906 0.0082 0.0723 0.2198 time 0.01
##
## ________________________________________________________________________________
## *** uDreyfus2_monmlp::monmlp.fit_BFGS ***
## monmlp monmlp.fit BFGS i 5 summary statistics 0.1135 0.0129 0.0898 0.2743 time 0.2
## monmlp monmlp.fit BFGS i 10 summary statistics 0.094 0.0088 0.0749 0.2448 time 0.19
## monmlp monmlp.fit BFGS i 15 summary statistics 0.0958 0.0092 0.0744 0.2245 time 0.19
## monmlp monmlp.fit BFGS i 20 summary statistics 0.1166 0.0136 0.0925 0.2785 time 0.18
##
## ________________________________________________________________________________
## *** uDreyfus2_nlsr::nlxb_none ***
## 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
## nlsr nlxb none i 5 summary statistics 0.0906 0.0082 0.0723 0.2197 time 0.05
## 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
## nlsr nlxb none i 10 summary statistics 0.0906 0.0082 0.0723 0.2197 time 0.06
## 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
## nlsr nlxb none i 15 summary statistics 0.0906 0.0082 0.0723 0.2197 time 0.05
## 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
## nlsr nlxb none i 20 summary statistics 0.0906 0.0082 0.0723 0.2197 time 0.06
##
## ________________________________________________________________________________
## *** uDreyfus2_nnet::nnet_none ***
## nnet nnet none i 5 summary statistics 0.0906 0.0082 0.0724 0.2198 time 0
## nnet nnet none i 10 summary statistics 0.2363 0.0558 0.1814 0.6649 time 0
## nnet nnet none i 15 summary statistics 0.0907 0.0082 0.0725 0.2202 time 0
## nnet nnet none i 20 summary statistics 0.1123 0.0126 0.0902 0.2738 time 0.02
##
## ________________________________________________________________________________
## *** uDreyfus2_qrnn::qrnn.fit_none ***
## qrnn qrnn.fit none i 5 summary statistics 0.1181 0.014 0.0875 0.3194 time 0.28
## qrnn qrnn.fit none i 10 summary statistics 0.1183 0.014 0.0902 0.2774 time 0.22
## qrnn qrnn.fit none i 15 summary statistics 0.0979 0.0096 0.0746 0.2304 time 0.11
## qrnn qrnn.fit none i 20 summary statistics 0.1299 0.0169 0.0988 0.3502 time 0.27
##
## ________________________________________________________________________________
## *** uDreyfus2_radiant.model::nn_none ***
## radiant.model nn none i 5 summary statistics 0.1138 0.013 0.0891 0.2762 time 0.02
## radiant.model nn none i 10 summary statistics 0.0915 0.0084 0.073 0.2318 time 0.03
## radiant.model nn none i 15 summary statistics 0.0909 0.0083 0.0725 0.2239 time 0.03
## radiant.model nn none i 20 summary statistics 0.0913 0.0083 0.0733 0.2341 time 0.03
##
## ________________________________________________________________________________
## *** uDreyfus2_rminer::fit_none ***
## rminer fit none i 5 summary statistics 0.0906 0.0082 0.0725 0.221 time 0.01
## rminer fit none i 10 summary statistics 0.0906 0.0082 0.0725 0.2205 time 0
## rminer fit none i 15 summary statistics 0.0906 0.0082 0.0724 0.2201 time 0.02
## rminer fit none i 20 summary statistics 0.0906 0.0082 0.0725 0.2199 time 0.02
##
## ________________________________________________________________________________
## *** uDreyfus2_validann::ann_BFGS ***
## initial value 53.045488
## iter 20 value 1.623734
## iter 40 value 0.334231
## iter 60 value 0.202669
## iter 80 value 0.163739
## iter 100 value 0.159093
## iter 120 value 0.158923
## iter 140 value 0.158807
## iter 160 value 0.158759
## iter 180 value 0.158730
## iter 200 value 0.158700
## final value 0.158700
## stopped after 200 iterations
## initial value 64.743158
## iter 20 value 1.276308
## iter 40 value 0.421110
## iter 60 value 0.408286
## iter 80 value 0.362024
## iter 100 value 0.296201
## iter 120 value 0.188784
## iter 140 value 0.161470
## iter 160 value 0.159012
## iter 180 value 0.158790
## iter 200 value 0.158745
## final value 0.158745
## stopped after 200 iterations
## initial value 70.520079
## iter 20 value 1.415925
## iter 40 value 0.415085
## iter 60 value 0.409430
## iter 80 value 0.395482
## iter 100 value 0.331399
## iter 120 value 0.280225
## iter 140 value 0.191394
## iter 160 value 0.163855
## iter 180 value 0.159653
## iter 200 value 0.158718
## final value 0.158718
## stopped after 200 iterations
## initial value 55.227593
## iter 20 value 2.465943
## iter 40 value 0.741206
## iter 60 value 0.435594
## iter 80 value 0.248888
## iter 100 value 0.244697
## iter 120 value 0.228864
## iter 140 value 0.178165
## iter 160 value 0.162163
## iter 180 value 0.158768
## iter 200 value 0.158683
## final value 0.158683
## stopped after 200 iterations
## initial value 73.289451
## iter 20 value 0.432810
## iter 40 value 0.290704
## iter 60 value 0.188189
## iter 80 value 0.160300
## iter 100 value 0.159545
## iter 120 value 0.159326
## iter 140 value 0.159090
## iter 160 value 0.158955
## iter 180 value 0.158818
## iter 200 value 0.158744
## final value 0.158744
## stopped after 200 iterations
## validann ann BFGS i 5 summary statistics 0.0906 0.0082 0.0725 0.2207 time 0.38
## initial value 60.068504
## iter 20 value 2.419831
## iter 40 value 1.897868
## iter 60 value 1.490464
## iter 80 value 0.679734
## iter 100 value 0.497347
## final value 0.497310
## converged
## initial value 81.788196
## iter 20 value 1.195285
## iter 40 value 0.282673
## iter 60 value 0.262917
## iter 80 value 0.253588
## iter 100 value 0.246910
## iter 120 value 0.243836
## iter 140 value 0.243667
## final value 0.243649
## converged
## initial value 87.056890
## iter 20 value 1.504181
## iter 40 value 0.367467
## iter 60 value 0.228657
## iter 80 value 0.170261
## iter 100 value 0.159124
## iter 120 value 0.158953
## iter 140 value 0.158810
## iter 160 value 0.158745
## iter 180 value 0.158722
## iter 200 value 0.158699
## final value 0.158699
## stopped after 200 iterations
## initial value 53.591260
## iter 20 value 0.616555
## iter 40 value 0.267704
## iter 60 value 0.187640
## iter 80 value 0.158988
## iter 100 value 0.158882
## iter 120 value 0.158786
## iter 140 value 0.158737
## iter 160 value 0.158712
## iter 180 value 0.158687
## iter 200 value 0.158669
## final value 0.158669
## stopped after 200 iterations
## initial value 72.672212
## iter 20 value 2.027595
## iter 40 value 0.392115
## iter 60 value 0.246212
## iter 80 value 0.164228
## iter 100 value 0.159253
## iter 120 value 0.158870
## iter 140 value 0.158790
## iter 160 value 0.158743
## iter 180 value 0.158715
## iter 200 value 0.158688
## final value 0.158688
## stopped after 200 iterations
## validann ann BFGS i 10 summary statistics 0.0906 0.0082 0.0724 0.2202 time 0.36
## initial value 54.490646
## iter 20 value 1.650252
## iter 40 value 0.360363
## iter 60 value 0.303531
## iter 80 value 0.225179
## iter 100 value 0.166967
## iter 120 value 0.158876
## iter 140 value 0.158675
## final value 0.158672
## converged
## initial value 76.552139
## iter 20 value 1.361852
## iter 40 value 0.182004
## iter 60 value 0.159386
## iter 80 value 0.158905
## iter 100 value 0.158803
## iter 120 value 0.158763
## iter 140 value 0.158723
## iter 160 value 0.158702
## iter 180 value 0.158682
## iter 200 value 0.158667
## final value 0.158667
## stopped after 200 iterations
## initial value 49.764127
## iter 20 value 2.715164
## iter 40 value 0.425663
## iter 60 value 0.265327
## iter 80 value 0.246757
## iter 100 value 0.244244
## iter 120 value 0.205465
## iter 140 value 0.169750
## iter 160 value 0.159963
## iter 180 value 0.158698
## final value 0.158681
## converged
## initial value 57.503249
## iter 20 value 2.355012
## iter 40 value 0.316626
## iter 60 value 0.181143
## iter 80 value 0.159115
## iter 100 value 0.158887
## iter 120 value 0.158797
## iter 140 value 0.158746
## iter 160 value 0.158719
## iter 180 value 0.158693
## iter 200 value 0.158669
## final value 0.158669
## stopped after 200 iterations
## initial value 54.329241
## iter 20 value 2.280528
## iter 40 value 1.488629
## iter 60 value 0.281539
## iter 80 value 0.168958
## iter 100 value 0.159357
## iter 120 value 0.159057
## iter 140 value 0.158885
## iter 160 value 0.158804
## iter 180 value 0.158751
## iter 200 value 0.158698
## final value 0.158698
## stopped after 200 iterations
## validann ann BFGS i 15 summary statistics 0.0906 0.0082 0.0724 0.2204 time 0.36
## initial value 54.986804
## iter 20 value 2.094802
## iter 40 value 0.237718
## iter 60 value 0.162343
## iter 80 value 0.158917
## iter 100 value 0.158836
## iter 120 value 0.158775
## iter 140 value 0.158737
## iter 160 value 0.158711
## iter 180 value 0.158685
## iter 200 value 0.158668
## final value 0.158668
## stopped after 200 iterations
## initial value 55.791988
## iter 20 value 2.151385
## iter 40 value 0.502543
## iter 60 value 0.229357
## iter 80 value 0.174706
## iter 100 value 0.159407
## iter 120 value 0.158794
## iter 140 value 0.158734
## iter 160 value 0.158690
## iter 180 value 0.158676
## iter 200 value 0.158660
## final value 0.158660
## stopped after 200 iterations
## initial value 43.194726
## iter 20 value 1.340272
## iter 40 value 0.451387
## iter 60 value 0.259057
## iter 80 value 0.244406
## iter 100 value 0.215524
## iter 120 value 0.169663
## iter 140 value 0.159801
## iter 160 value 0.158678
## iter 180 value 0.158665
## iter 200 value 0.158651
## final value 0.158651
## stopped after 200 iterations
## initial value 102.611812
## iter 20 value 2.613609
## iter 40 value 1.555493
## iter 60 value 0.290364
## iter 80 value 0.180476
## iter 100 value 0.173694
## iter 120 value 0.166346
## iter 140 value 0.161670
## iter 160 value 0.159202
## iter 180 value 0.158605
## final value 0.158541
## converged
## initial value 41.209336
## iter 20 value 1.790961
## iter 40 value 0.175909
## iter 60 value 0.159317
## iter 80 value 0.158785
## iter 100 value 0.158750
## iter 120 value 0.158715
## iter 140 value 0.158694
## iter 160 value 0.158678
## final value 0.158672
## converged
## validann ann BFGS i 20 summary statistics 0.0906 0.0082 0.0724 0.2198 time 0.29
##
## ________________________________________________________________________________
## *** uDreyfus2_validann::ann_L-BFGS-B ***
## iter 20 value 2.273826
## iter 40 value 0.532132
## iter 60 value 0.458250
## iter 80 value 0.444727
## iter 100 value 0.417045
## iter 120 value 0.411758
## iter 140 value 0.410393
## iter 160 value 0.410137
## iter 180 value 0.409867
## iter 200 value 0.409070
## final value 0.408786
## stopped after 201 iterations
## iter 20 value 1.178794
## iter 40 value 0.453680
## iter 60 value 0.393473
## iter 80 value 0.236507
## iter 100 value 0.203618
## iter 120 value 0.195590
## iter 140 value 0.169216
## iter 160 value 0.163893
## iter 180 value 0.163395
## iter 200 value 0.163184
## final value 0.163174
## stopped after 201 iterations
## iter 20 value 1.984546
## iter 40 value 0.293738
## iter 60 value 0.177055
## iter 80 value 0.167495
## iter 100 value 0.160675
## iter 120 value 0.159302
## iter 140 value 0.159177
## iter 160 value 0.159079
## iter 180 value 0.158975
## iter 200 value 0.158899
## final value 0.158899
## stopped after 201 iterations
## iter 20 value 2.277411
## iter 40 value 0.440199
## iter 60 value 0.196449
## iter 80 value 0.163074
## iter 100 value 0.162094
## iter 120 value 0.161489
## iter 140 value 0.160277
## iter 160 value 0.159948
## iter 180 value 0.159564
## iter 200 value 0.159367
## final value 0.159365
## stopped after 201 iterations
## iter 20 value 2.427442
## iter 40 value 1.718195
## iter 60 value 0.928596
## iter 80 value 0.224566
## iter 100 value 0.190321
## iter 120 value 0.186690
## iter 140 value 0.185649
## iter 160 value 0.179508
## iter 180 value 0.175799
## iter 200 value 0.169616
## final value 0.168765
## stopped after 201 iterations
## validann ann L-BFGS-B i 5 summary statistics 0.0935 0.0087 0.0736 0.2277 time 0.42
## iter 20 value 2.277077
## iter 40 value 0.508216
## iter 60 value 0.421351
## iter 80 value 0.411961
## iter 100 value 0.409616
## iter 120 value 0.407943
## iter 140 value 0.403646
## iter 160 value 0.371295
## iter 180 value 0.320491
## iter 200 value 0.250775
## final value 0.247778
## stopped after 201 iterations
## iter 20 value 2.333333
## iter 40 value 1.027884
## iter 60 value 0.326586
## iter 80 value 0.217354
## iter 100 value 0.203310
## iter 120 value 0.174056
## iter 140 value 0.165013
## iter 160 value 0.163198
## iter 180 value 0.162390
## iter 200 value 0.161233
## final value 0.161191
## stopped after 201 iterations
## iter 20 value 1.383434
## iter 40 value 0.253055
## iter 60 value 0.195250
## iter 80 value 0.182778
## iter 100 value 0.170605
## iter 120 value 0.164530
## iter 140 value 0.163683
## iter 160 value 0.161631
## iter 180 value 0.160041
## iter 200 value 0.159777
## final value 0.159771
## stopped after 201 iterations
## iter 20 value 2.498400
## iter 40 value 0.502724
## iter 60 value 0.446966
## iter 80 value 0.415621
## iter 100 value 0.411393
## iter 120 value 0.410397
## iter 140 value 0.410234
## iter 160 value 0.410114
## iter 180 value 0.409782
## iter 200 value 0.409679
## final value 0.409672
## stopped after 201 iterations
## iter 20 value 2.330838
## iter 40 value 0.486325
## iter 60 value 0.446408
## iter 80 value 0.428233
## iter 100 value 0.411732
## iter 120 value 0.409407
## iter 140 value 0.382247
## iter 160 value 0.282909
## iter 180 value 0.231926
## iter 200 value 0.210059
## final value 0.208168
## stopped after 201 iterations
## validann ann L-BFGS-B i 10 summary statistics 0.1038 0.0108 0.0821 0.2319 time 0.45
## iter 20 value 1.599026
## iter 40 value 0.372012
## iter 60 value 0.190971
## iter 80 value 0.182833
## iter 100 value 0.170968
## iter 120 value 0.168092
## iter 140 value 0.162616
## iter 160 value 0.161838
## iter 180 value 0.161560
## iter 200 value 0.161071
## final value 0.161058
## stopped after 201 iterations
## iter 20 value 2.330415
## iter 40 value 0.425111
## iter 60 value 0.420026
## iter 80 value 0.419323
## iter 100 value 0.418651
## iter 120 value 0.416847
## iter 140 value 0.415288
## iter 160 value 0.414596
## iter 180 value 0.414061
## iter 200 value 0.412852
## final value 0.412766
## stopped after 201 iterations
## iter 20 value 2.167831
## iter 40 value 0.398677
## iter 60 value 0.238595
## iter 80 value 0.202770
## iter 100 value 0.178384
## iter 120 value 0.165211
## iter 140 value 0.163368
## iter 160 value 0.163049
## iter 180 value 0.161716
## iter 200 value 0.161410
## final value 0.161405
## stopped after 201 iterations
## iter 20 value 1.635162
## iter 40 value 0.510457
## iter 60 value 0.448771
## iter 80 value 0.260307
## iter 100 value 0.222191
## iter 120 value 0.205912
## iter 140 value 0.197424
## iter 160 value 0.178645
## iter 180 value 0.161774
## iter 200 value 0.159592
## final value 0.159577
## stopped after 201 iterations
## iter 20 value 2.538080
## iter 40 value 1.353008
## iter 60 value 0.329655
## iter 80 value 0.321741
## iter 100 value 0.318733
## iter 120 value 0.314735
## iter 140 value 0.312270
## iter 160 value 0.300361
## iter 180 value 0.266487
## iter 200 value 0.263877
## final value 0.263794
## stopped after 201 iterations
## validann ann L-BFGS-B i 15 summary statistics 0.1168 0.0137 0.0928 0.2687 time 0.39
## iter 20 value 2.301681
## iter 40 value 0.486048
## iter 60 value 0.412882
## iter 80 value 0.412708
## iter 100 value 0.412504
## iter 120 value 0.407780
## iter 140 value 0.352022
## iter 160 value 0.307045
## iter 180 value 0.271474
## iter 200 value 0.235997
## final value 0.235777
## stopped after 201 iterations
## iter 20 value 1.928000
## iter 40 value 0.324728
## iter 60 value 0.198091
## iter 80 value 0.176917
## iter 100 value 0.165666
## iter 120 value 0.162127
## iter 140 value 0.160963
## iter 160 value 0.160388
## iter 180 value 0.160118
## iter 200 value 0.159666
## final value 0.159637
## stopped after 201 iterations
## iter 20 value 2.324925
## iter 40 value 0.619329
## iter 60 value 0.485693
## iter 80 value 0.485014
## iter 100 value 0.483910
## iter 120 value 0.482128
## iter 140 value 0.482013
## iter 160 value 0.481946
## iter 180 value 0.481865
## iter 200 value 0.481736
## final value 0.481734
## stopped after 201 iterations
## iter 20 value 1.599988
## iter 40 value 0.264057
## iter 60 value 0.186130
## iter 80 value 0.178822
## iter 100 value 0.165674
## iter 120 value 0.162260
## iter 140 value 0.161874
## iter 160 value 0.161460
## iter 180 value 0.161082
## iter 200 value 0.160446
## final value 0.160427
## stopped after 201 iterations
## iter 20 value 2.021101
## iter 40 value 0.525636
## iter 60 value 0.423183
## iter 80 value 0.409691
## iter 100 value 0.352365
## iter 120 value 0.315883
## iter 140 value 0.288974
## iter 160 value 0.261366
## iter 180 value 0.238946
## iter 200 value 0.226087
## final value 0.225965
## stopped after 201 iterations
## validann ann L-BFGS-B i 20 summary statistics 0.1081 0.0117 0.0858 0.241 time 0.4
##
## ________________________________________________________________________________
## *** uGauss1_brnn::brnn_gaussNewton ***
## Number of parameters (weights and biases) to estimate: 15
## Nguyen-Widrow method
## Scaling factor= 3.5
## gamma= 14.3027 alpha= 0.0311 beta= 162.9203
## Number of parameters (weights and biases) to estimate: 15
## Nguyen-Widrow method
## Scaling factor= 3.5
## gamma= 13.3009 alpha= 0.0183 beta= 141.0339
## Number of parameters (weights and biases) to estimate: 15
## Nguyen-Widrow method
## Scaling factor= 3.5
## gamma= 14.2999 alpha= 0.0311 beta= 162.9339
## Number of parameters (weights and biases) to estimate: 15
## Nguyen-Widrow method
## Scaling factor= 3.5
## gamma= 13.2537 alpha= 0.0178 beta= 139.3681
## Number of parameters (weights and biases) to estimate: 15
## Nguyen-Widrow method
## Scaling factor= 3.5
## gamma= 13.0928 alpha= 0.0224 beta= 113.163
## brnn brnn gaussNewton i 5 summary statistics 2.6989 7.2838 2.123 8.4143 time 0.06
## Number of parameters (weights and biases) to estimate: 15
## Nguyen-Widrow method
## Scaling factor= 3.5
## gamma= 13.6883 alpha= 0.0067 beta= 18.5558
## Number of parameters (weights and biases) to estimate: 15
## Nguyen-Widrow method
## Scaling factor= 3.5
## gamma= 13.2604 alpha= 0.0177 beta= 139.4029
## Number of parameters (weights and biases) to estimate: 15
## Nguyen-Widrow method
## Scaling factor= 3.5
## gamma= 13.2775 alpha= 0.018 beta= 139.2512
## 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= 13.0307 alpha= 0.0215 beta= 113.4749
## brnn brnn gaussNewton i 10 summary statistics 2.6955 7.2657 2.1199 8.4098 time 0.05
## 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= 14.2978 alpha= 0.031 beta= 162.9428
## Number of parameters (weights and biases) to estimate: 15
## Nguyen-Widrow method
## Scaling factor= 3.5
## gamma= 13.3009 alpha= 0.0183 beta= 141.0339
## Number of parameters (weights and biases) to estimate: 15
## Nguyen-Widrow method
## Scaling factor= 3.5
## gamma= 13.1708 alpha= 0.0015 beta= 42.8678
## Number of parameters (weights and biases) to estimate: 15
## Nguyen-Widrow method
## Scaling factor= 3.5
## gamma= 14.6972 alpha= 0.0495 beta= 157.2597
## brnn brnn gaussNewton i 15 summary statistics 2.2816 5.2059 1.7842 7.4559 time 0.03
## Number of parameters (weights and biases) to estimate: 15
## Nguyen-Widrow method
## Scaling factor= 3.5
## gamma= 14.7389 alpha= 0.0496 beta= 157.3081
## 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= 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
## brnn brnn gaussNewton i 20 summary statistics 2.2526 5.0741 1.7489 7.2646 time 0.01
##
## ________________________________________________________________________________
## *** uGauss1_CaDENCE::cadence.fit_optim ***
## n.hidden = 5 --> 1 * NLL = -264.6282 ; penalty = 0; BIC = -407.7843 ; AICc = -480.7983 ; AIC = -485.2565
## n.hidden = 5 --> 1 * NLL = -237.9421 ; penalty = 0; BIC = -354.412 ; AICc = -427.426 ; AIC = -431.8841
## n.hidden = 5 --> 1 * NLL = -328.8062 ; penalty = 0; BIC = -536.1403 ; AICc = -609.1542 ; AIC = -613.6124
## n.hidden = 5 --> 1 * NLL = -163.9417 ; penalty = 0; BIC = -206.4114 ; AICc = -279.4253 ; AIC = -283.8835
## n.hidden = 5 --> 1 * NLL = -85.16584 ; penalty = 0; BIC = -48.85953 ; AICc = -121.8735 ; AIC = -126.3317
## CaDENCE cadence.fit optim i 5 summary statistics 21.1914 449.0774 13.2783 49.9159 time 2.89
## n.hidden = 5 --> 1 * NLL = -370.1964 ; penalty = 0; BIC = -618.9206 ; AICc = -691.9346 ; AIC = -696.3927
## n.hidden = 5 --> 1 * NLL = 71.81933 ; penalty = 0; BIC = 265.1108 ; AICc = 192.0968 ; AIC = 187.6387
## n.hidden = 5 --> 1 * NLL = 46.7548 ; penalty = 0; BIC = 214.9817 ; AICc = 141.9677 ; AIC = 137.5096
## n.hidden = 5 --> 1 * NLL = -342.6498 ; penalty = 0; BIC = -563.8274 ; AICc = -636.8414 ; AIC = -641.2996
## n.hidden = 5 --> 1 * NLL = -22.41603 ; penalty = 0; BIC = 76.64007 ; AICc = 3.626082 ; AIC = -0.8320681
## CaDENCE cadence.fit optim i 10 summary statistics 37.0174 1370.289 21.3326 98.5726 time 3.03
## n.hidden = 5 --> 1 * NLL = -326.1997 ; penalty = 0; BIC = -530.9273 ; AICc = -603.9413 ; AIC = -608.3995
## n.hidden = 5 --> 1 * NLL = -326.5287 ; penalty = 0; BIC = -531.5853 ; AICc = -604.5993 ; AIC = -609.0574
## n.hidden = 5 --> 1 * NLL = -325.4457 ; penalty = 0; BIC = -529.4193 ; AICc = -602.4333 ; AIC = -606.8914
## n.hidden = 5 --> 1 * NLL = -339.6256 ; penalty = 0; BIC = -557.779 ; AICc = -630.793 ; AIC = -635.2511
## n.hidden = 5 --> 1 * NLL = -322.6782 ; penalty = 0; BIC = -523.8842 ; AICc = -596.8982 ; AIC = -601.3564
## CaDENCE cadence.fit optim i 15 summary statistics 3.1584 9.9754 2.4059 12.0333 time 2.89
## n.hidden = 5 --> 1 * NLL = -128.3715 ; penalty = 0; BIC = -135.2708 ; AICc = -208.2848 ; AIC = -212.743
## n.hidden = 5 --> 1 * NLL = -172.8786 ; penalty = 0; BIC = -224.2851 ; AICc = -297.2991 ; AIC = -301.7572
## n.hidden = 5 --> 1 * NLL = -95.61207 ; penalty = 0; BIC = -69.752 ; AICc = -142.766 ; AIC = -147.2241
## n.hidden = 5 --> 1 * NLL = -326.7983 ; penalty = 0; BIC = -532.1245 ; AICc = -605.1385 ; AIC = -609.5967
## n.hidden = 5 --> 1 * NLL = -291.5572 ; penalty = 0; BIC = -461.6424 ; AICc = -534.6563 ; AIC = -539.1145
## CaDENCE cadence.fit optim i 20 summary statistics 3.5896 12.8855 2.7435 12.0382 time 3.03
##
## ________________________________________________________________________________
## *** uGauss1_MachineShop::fit_none ***
## MachineShop fit none i 5 summary statistics 2.3272 5.4158 1.8206 7.3035 time 0.04
## MachineShop fit none i 10 summary statistics 2.2481 5.054 1.7463 7.1889 time 0.01
## MachineShop fit none i 15 summary statistics 2.3193 5.3792 1.8207 7.1811 time 0.02
## MachineShop fit none i 20 summary statistics 2.2371 5.0045 1.738 7.0113 time 0.03
##
## ________________________________________________________________________________
## *** uGauss1_minpack.lm::nlsLM_none ***
## minpack.lm nlsLM none i 5 summary statistics 2.6121 6.8229 2.0474 8.2488 time 0.07
## minpack.lm nlsLM none i 10 summary statistics 2.2379 5.0084 1.7396 7.0056 time 0.06
## minpack.lm nlsLM none i 15 summary statistics 2.3352 5.4534 1.8228 7.6159 time 0.08
## minpack.lm nlsLM none i 20 summary statistics 2.2276 4.9622 1.7247 7.4029 time 0.08
##
## ________________________________________________________________________________
## *** uGauss1_monmlp::monmlp.fit_BFGS ***
## monmlp monmlp.fit BFGS i 5 summary statistics 2.8902 8.353 2.3039 9.1028 time 0.21
## monmlp monmlp.fit BFGS i 10 summary statistics 3.1754 10.0833 2.5701 9.9232 time 0.21
## monmlp monmlp.fit BFGS i 15 summary statistics 2.9018 8.4206 2.3116 8.6395 time 0.23
## monmlp monmlp.fit BFGS i 20 summary statistics 2.9172 8.5098 2.3199 9.8324 time 0.21
##
## ________________________________________________________________________________
## *** uGauss1_nlsr::nlxb_none ***
## 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
## nlsr nlxb none i 5 summary statistics 3.343 11.1755 2.7537 8.2468 time 0.16
## 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
## nlsr nlxb none i 10 summary statistics 2.2323 4.9832 1.7379 6.9359 time 0.16
## 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
## nlsr nlxb none i 15 summary statistics 2.2338 4.9897 1.7377 6.948 time 0.16
## 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
## nlsr nlxb none i 20 summary statistics 14.6558 214.7935 8.9346 44.981 time 0.14
##
## ________________________________________________________________________________
## *** uGauss1_nnet::nnet_none ***
## nnet nnet none i 5 summary statistics 6.7492 45.5521 5.558 18.6571 time 0.04
## nnet nnet none i 10 summary statistics 2.7083 7.335 2.1332 8.5231 time 0.03
## nnet nnet none i 15 summary statistics 5.1253 26.2686 3.9698 15.3912 time 0.03
## nnet nnet none i 20 summary statistics 2.2636 5.1237 1.7749 7.0643 time 0.01
##
## ________________________________________________________________________________
## *** uGauss1_qrnn::qrnn.fit_none ***
## qrnn qrnn.fit none i 5 summary statistics 4.4078 19.4286 3.419 13.9335 time 0.36
## qrnn qrnn.fit none i 10 summary statistics 10.7078 114.6563 8.3026 25.6897 time 0.22
## qrnn qrnn.fit none i 15 summary statistics 8.1213 65.9557 6.3265 23.6297 time 0.28
## qrnn qrnn.fit none i 20 summary statistics 15.2533 232.6619 8.8155 55.4369 time 0.2
##
## ________________________________________________________________________________
## *** uGauss1_radiant.model::nn_none ***
## radiant.model nn none i 5 summary statistics 2.3207 5.3857 1.8181 7.1938 time 0.03
## radiant.model nn none i 10 summary statistics 6.0737 36.8898 4.7761 16.9481 time 0.05
## radiant.model nn none i 15 summary statistics 2.2779 5.1887 1.7905 7.4729 time 0.05
## radiant.model nn none i 20 summary statistics 2.3336 5.4459 1.8372 7.2492 time 0.04
##
## ________________________________________________________________________________
## *** uGauss1_rminer::fit_none ***
## rminer fit none i 5 summary statistics 2.4166 5.8402 1.8994 7.6213 time 0.1
## rminer fit none i 10 summary statistics 2.3028 5.3028 1.8129 7.3151 time 0.08
## rminer fit none i 15 summary statistics 2.2462 5.0455 1.73 7.5835 time 0.09
## rminer fit none i 20 summary statistics 2.2395 5.0154 1.7383 6.9481 time 0.08
##
## ________________________________________________________________________________
## *** uGauss1_validann::ann_BFGS ***
## initial value 199.613544
## iter 20 value 64.940426
## iter 40 value 45.251947
## iter 60 value 3.537065
## iter 80 value 1.121607
## iter 100 value 0.795769
## iter 120 value 0.758866
## iter 140 value 0.738489
## iter 160 value 0.737581
## iter 180 value 0.736794
## iter 200 value 0.736020
## final value 0.736020
## stopped after 200 iterations
## initial value 268.687586
## iter 20 value 71.641524
## iter 40 value 31.777613
## iter 60 value 10.279181
## iter 80 value 2.026908
## iter 100 value 1.080471
## iter 120 value 1.058960
## iter 140 value 1.031675
## iter 160 value 1.028875
## iter 180 value 1.021573
## iter 200 value 1.018925
## final value 1.018925
## stopped after 200 iterations
## initial value 313.856271
## iter 20 value 51.410910
## iter 40 value 14.984513
## iter 60 value 3.958355
## iter 80 value 1.616110
## iter 100 value 1.150831
## iter 120 value 1.043982
## iter 140 value 0.945960
## iter 160 value 0.849303
## iter 180 value 0.762520
## iter 200 value 0.750323
## final value 0.750323
## stopped after 200 iterations
## initial value 399.705815
## iter 20 value 69.879963
## iter 40 value 47.492419
## iter 60 value 34.484806
## iter 80 value 13.059447
## iter 100 value 1.750899
## iter 120 value 1.588176
## iter 140 value 1.299307
## iter 160 value 1.116662
## iter 180 value 1.088211
## iter 200 value 1.053839
## final value 1.053839
## stopped after 200 iterations
## initial value 359.987196
## iter 20 value 56.399443
## iter 40 value 15.551478
## iter 60 value 3.450227
## iter 80 value 0.813840
## iter 100 value 0.780465
## iter 120 value 0.772586
## iter 140 value 0.745481
## iter 160 value 0.735210
## iter 180 value 0.734594
## iter 200 value 0.733444
## final value 0.733444
## stopped after 200 iterations
## validann ann BFGS i 5 summary statistics 2.2591 5.1037 1.7519 7.4128 time 0.78
## initial value 287.959363
## iter 20 value 54.623165
## iter 40 value 20.531015
## iter 60 value 9.868946
## iter 80 value 7.210665
## iter 100 value 6.673283
## iter 120 value 6.489109
## iter 140 value 6.335805
## iter 160 value 6.274318
## iter 180 value 6.227702
## iter 200 value 6.213567
## final value 6.213567
## stopped after 200 iterations
## initial value 330.712123
## iter 20 value 69.863856
## iter 40 value 35.701732
## iter 60 value 20.323704
## iter 80 value 9.859329
## iter 100 value 1.419476
## iter 120 value 0.952692
## iter 140 value 0.733867
## iter 160 value 0.733017
## iter 180 value 0.732828
## iter 200 value 0.732538
## final value 0.732538
## stopped after 200 iterations
## initial value 436.863620
## iter 20 value 41.796346
## iter 40 value 19.249870
## iter 60 value 8.545174
## iter 80 value 1.414967
## iter 100 value 1.009761
## iter 120 value 0.809877
## iter 140 value 0.789569
## iter 160 value 0.787176
## iter 180 value 0.781816
## iter 200 value 0.780845
## final value 0.780845
## stopped after 200 iterations
## initial value 293.104392
## iter 20 value 31.458347
## iter 40 value 17.006336
## iter 60 value 12.779417
## iter 80 value 9.009817
## iter 100 value 6.790865
## iter 120 value 6.593226
## iter 140 value 6.263609
## iter 160 value 6.185232
## iter 180 value 6.140616
## iter 200 value 6.101816
## final value 6.101816
## stopped after 200 iterations
## initial value 250.697984
## iter 20 value 52.345310
## iter 40 value 7.685672
## iter 60 value 1.347136
## iter 80 value 0.911312
## iter 100 value 0.787069
## iter 120 value 0.777613
## iter 140 value 0.768501
## iter 160 value 0.763030
## iter 180 value 0.752813
## iter 200 value 0.727460
## final value 0.727460
## stopped after 200 iterations
## validann ann BFGS i 10 summary statistics 2.2499 5.062 1.7514 7.2657 time 0.81
## initial value 243.591293
## iter 20 value 66.790128
## iter 40 value 19.046561
## iter 60 value 13.328464
## iter 80 value 10.647273
## iter 100 value 8.354528
## iter 120 value 7.451931
## iter 140 value 6.767538
## iter 160 value 6.651903
## iter 180 value 6.455464
## iter 200 value 6.397789
## final value 6.397789
## stopped after 200 iterations
## initial value 208.416722
## iter 20 value 51.824792
## iter 40 value 3.011607
## iter 60 value 1.560072
## iter 80 value 1.235462
## iter 100 value 1.123130
## iter 120 value 1.083720
## iter 140 value 1.045784
## iter 160 value 1.036298
## iter 180 value 1.030031
## iter 200 value 1.025604
## final value 1.025604
## stopped after 200 iterations
## initial value 287.584639
## iter 20 value 40.238302
## iter 40 value 27.063457
## iter 60 value 10.167531
## iter 80 value 7.267991
## iter 100 value 6.757664
## iter 120 value 6.667535
## iter 140 value 6.526240
## iter 160 value 6.434504
## iter 180 value 6.386068
## iter 200 value 6.373549
## final value 6.373549
## stopped after 200 iterations
## initial value 275.741511
## iter 20 value 31.534920
## iter 40 value 10.599729
## iter 60 value 3.738735
## iter 80 value 2.248717
## iter 100 value 1.181245
## iter 120 value 1.107835
## iter 140 value 1.070211
## iter 160 value 1.046263
## iter 180 value 1.025438
## iter 200 value 0.846921
## final value 0.846921
## stopped after 200 iterations
## initial value 336.383978
## iter 20 value 37.801967
## iter 40 value 1.924194
## iter 60 value 0.795945
## iter 80 value 0.781863
## iter 100 value 0.778877
## iter 120 value 0.767939
## iter 140 value 0.738216
## iter 160 value 0.730534
## iter 180 value 0.729708
## iter 200 value 0.729393
## final value 0.729393
## stopped after 200 iterations
## validann ann BFGS i 15 summary statistics 2.2529 5.0755 1.7503 7.2465 time 0.78
## initial value 259.241864
## iter 20 value 43.330972
## iter 40 value 16.620950
## iter 60 value 6.323769
## iter 80 value 5.315165
## iter 100 value 4.636669
## iter 120 value 3.941728
## iter 140 value 2.502704
## iter 160 value 1.305135
## iter 180 value 1.032940
## iter 200 value 0.828109
## final value 0.828109
## stopped after 200 iterations
## initial value 330.450611
## iter 20 value 51.823606
## iter 40 value 14.649851
## iter 60 value 0.896690
## iter 80 value 0.813212
## iter 100 value 0.786901
## iter 120 value 0.784120
## iter 140 value 0.779730
## iter 160 value 0.777861
## iter 180 value 0.777520
## iter 200 value 0.775815
## final value 0.775815
## stopped after 200 iterations
## initial value 344.525922
## iter 20 value 80.010392
## iter 40 value 26.913894
## iter 60 value 16.151783
## iter 80 value 7.185652
## iter 100 value 1.814339
## iter 120 value 1.149086
## iter 140 value 1.083688
## iter 160 value 1.048063
## iter 180 value 1.036958
## iter 200 value 1.021770
## final value 1.021770
## stopped after 200 iterations
## initial value 337.991009
## iter 20 value 101.487696
## iter 40 value 33.073833
## iter 60 value 11.116084
## iter 80 value 1.505005
## iter 100 value 0.966470
## iter 120 value 0.852109
## iter 140 value 0.823551
## iter 160 value 0.809082
## iter 180 value 0.801906
## iter 200 value 0.798802
## final value 0.798802
## stopped after 200 iterations
## initial value 325.887445
## iter 20 value 60.229090
## iter 40 value 34.812674
## iter 60 value 29.908959
## iter 80 value 28.494710
## iter 100 value 28.106535
## iter 120 value 27.215467
## iter 140 value 27.213283
## iter 160 value 27.164375
## iter 180 value 21.936244
## iter 200 value 4.551577
## final value 4.551577
## stopped after 200 iterations
## validann ann BFGS i 20 summary statistics 5.6278 31.6722 4.6571 14.8666 time 0.96
##
## ________________________________________________________________________________
## *** uGauss1_validann::ann_L-BFGS-B ***
## iter 20 value 49.044203
## iter 40 value 38.723901
## iter 60 value 35.482766
## iter 80 value 32.671320
## iter 100 value 30.931411
## iter 120 value 30.390226
## iter 140 value 29.017990
## iter 160 value 21.993835
## iter 180 value 17.910636
## iter 200 value 12.924690
## final value 12.866449
## stopped after 201 iterations
## iter 20 value 39.268573
## iter 40 value 29.305786
## iter 60 value 14.877770
## iter 80 value 3.519571
## iter 100 value 2.088193
## iter 120 value 1.690834
## iter 140 value 1.472766
## iter 160 value 1.295885
## iter 180 value 1.280027
## iter 200 value 1.233586
## final value 1.233114
## stopped after 201 iterations
## iter 20 value 69.992213
## iter 40 value 38.582699
## iter 60 value 13.490936
## iter 80 value 11.979816
## iter 100 value 11.428653
## iter 120 value 10.702538
## iter 140 value 10.399932
## iter 160 value 9.937866
## iter 180 value 9.735727
## iter 200 value 9.562558
## final value 9.554957
## stopped after 201 iterations
## iter 20 value 66.958496
## iter 40 value 19.896520
## iter 60 value 14.583051
## iter 80 value 11.825776
## iter 100 value 4.413596
## iter 120 value 3.037761
## iter 140 value 2.011395
## iter 160 value 1.720927
## iter 180 value 1.657186
## iter 200 value 1.624205
## final value 1.619475
## stopped after 201 iterations
## iter 20 value 86.521900
## iter 40 value 24.601180
## iter 60 value 3.725068
## iter 80 value 2.548767
## iter 100 value 1.406663
## iter 120 value 1.092685
## iter 140 value 0.925216
## iter 160 value 0.838622
## iter 180 value 0.822861
## iter 200 value 0.789492
## final value 0.788642
## stopped after 201 iterations
## validann ann L-BFGS-B i 5 summary statistics 2.3426 5.4878 1.8182 7.4934 time 0.84
## iter 20 value 42.357858
## iter 40 value 23.037635
## iter 60 value 14.713435
## iter 80 value 12.609705
## iter 100 value 10.287053
## iter 120 value 7.831879
## iter 140 value 1.919001
## iter 160 value 1.515558
## iter 180 value 1.101770
## iter 200 value 1.010068
## final value 1.009584
## stopped after 201 iterations
## iter 20 value 48.051999
## iter 40 value 38.873493
## iter 60 value 25.166561
## iter 80 value 12.401167
## iter 100 value 10.224705
## iter 120 value 3.523682
## iter 140 value 2.195973
## iter 160 value 1.899962
## iter 180 value 1.631980
## iter 200 value 1.213153
## final value 1.206528
## stopped after 201 iterations
## iter 20 value 56.714615
## iter 40 value 23.459354
## iter 60 value 13.416704
## iter 80 value 11.194881
## iter 100 value 8.400338
## iter 120 value 3.183387
## iter 140 value 1.116318
## iter 160 value 0.878852
## iter 180 value 0.868246
## iter 200 value 0.833247
## final value 0.832694
## stopped after 201 iterations
## iter 20 value 50.555660
## iter 40 value 29.116944
## iter 60 value 18.781380
## iter 80 value 17.383410
## iter 100 value 17.161240
## iter 120 value 17.052029
## iter 140 value 16.807280
## iter 160 value 16.615040
## iter 180 value 16.413238
## iter 200 value 16.257462
## final value 16.244323
## stopped after 201 iterations
## iter 20 value 57.562682
## iter 40 value 32.295817
## iter 60 value 31.659525
## iter 80 value 27.879840
## iter 100 value 18.238250
## iter 120 value 13.986694
## iter 140 value 13.205216
## iter 160 value 12.718794
## iter 180 value 12.518127
## iter 200 value 12.430222
## final value 12.406058
## stopped after 201 iterations
## validann ann L-BFGS-B i 10 summary statistics 9.2913 86.3277 7.1195 28.0208 time 0.89
## iter 20 value 56.629552
## iter 40 value 20.071546
## iter 60 value 4.253214
## iter 80 value 2.184442
## iter 100 value 1.660250
## iter 120 value 1.600407
## iter 140 value 1.524879
## iter 160 value 1.511433
## iter 180 value 1.451480
## iter 200 value 1.398995
## final value 1.398482
## stopped after 201 iterations
## iter 20 value 53.611056
## iter 40 value 21.851961
## iter 60 value 12.760217
## iter 80 value 9.453639
## iter 100 value 8.643187
## iter 120 value 5.631173
## iter 140 value 3.695872
## iter 160 value 2.770882
## iter 180 value 2.497540
## iter 200 value 1.869510
## final value 1.866007
## stopped after 201 iterations
## iter 20 value 37.743934
## iter 40 value 33.286228
## iter 60 value 22.673077
## iter 80 value 19.670353
## iter 100 value 18.666008
## iter 120 value 15.983682
## iter 140 value 4.384888
## iter 160 value 2.758274
## iter 180 value 2.350613
## iter 200 value 1.921911
## final value 1.890913
## stopped after 201 iterations
## iter 20 value 42.352554
## iter 40 value 33.037109
## iter 60 value 28.389140
## iter 80 value 11.973506
## iter 100 value 8.379445
## iter 120 value 3.091378
## iter 140 value 2.503434
## iter 160 value 2.283141
## iter 180 value 2.004404
## iter 200 value 1.713744
## final value 1.699836
## stopped after 201 iterations
## iter 20 value 46.101529
## iter 40 value 31.839791
## iter 60 value 26.219807
## iter 80 value 19.982806
## iter 100 value 16.322103
## iter 120 value 15.295852
## iter 140 value 14.530462
## iter 160 value 14.115072
## iter 180 value 14.097259
## iter 200 value 14.077644
## final value 14.076569
## stopped after 201 iterations
## validann ann L-BFGS-B i 15 summary statistics 9.8971 97.9519 7.0541 29.6291 time 0.86
## iter 20 value 36.262755
## iter 40 value 26.266733
## iter 60 value 22.513253
## iter 80 value 18.801022
## iter 100 value 10.709905
## iter 120 value 5.124852
## iter 140 value 3.096555
## iter 160 value 2.767169
## iter 180 value 2.302589
## iter 200 value 1.820486
## final value 1.807683
## stopped after 201 iterations
## iter 20 value 81.018027
## iter 40 value 31.366982
## iter 60 value 10.565276
## iter 80 value 2.858830
## iter 100 value 2.049654
## iter 120 value 1.667489
## iter 140 value 1.555224
## iter 160 value 1.426736
## iter 180 value 1.391763
## iter 200 value 1.327326
## final value 1.322717
## stopped after 201 iterations
## iter 20 value 71.603978
## iter 40 value 24.771347
## iter 60 value 17.120139
## iter 80 value 14.705656
## iter 100 value 14.280576
## iter 120 value 12.971407
## iter 140 value 11.337259
## iter 160 value 9.367353
## iter 180 value 9.149466
## iter 200 value 8.867315
## final value 8.827724
## stopped after 201 iterations
## iter 20 value 43.264135
## iter 40 value 35.393912
## iter 60 value 21.514079
## iter 80 value 18.622662
## iter 100 value 16.318863
## iter 120 value 4.884072
## iter 140 value 2.563207
## iter 160 value 1.804945
## iter 180 value 1.522459
## iter 200 value 1.476716
## final value 1.475768
## stopped after 201 iterations
## iter 20 value 85.788068
## iter 40 value 48.110980
## iter 60 value 10.114705
## iter 80 value 5.161659
## iter 100 value 1.561310
## iter 120 value 1.103738
## iter 140 value 1.046066
## iter 160 value 0.880360
## iter 180 value 0.855493
## iter 200 value 0.841713
## final value 0.841380
## stopped after 201 iterations
## validann ann L-BFGS-B i 20 summary statistics 2.4197 5.8547 1.9296 7.4555 time 0.86
##
## ________________________________________________________________________________
## *** uGauss2_brnn::brnn_gaussNewton ***
## Number of parameters (weights and biases) to estimate: 12
## Nguyen-Widrow method
## Scaling factor= 2.8
## gamma= 11.4697 alpha= 0.0212 beta= 120.2874
## Number of parameters (weights and biases) to estimate: 12
## Nguyen-Widrow method
## Scaling factor= 2.8
## gamma= 11.4698 alpha= 0.0212 beta= 120.2876
## Number of parameters (weights and biases) to estimate: 12
## Nguyen-Widrow method
## Scaling factor= 2.8
## gamma= 11.4697 alpha= 0.0212 beta= 120.2874
## Number of parameters (weights and biases) to estimate: 12
## Nguyen-Widrow method
## Scaling factor= 2.8
## gamma= 10.932 alpha= 0.0207 beta= 24.2931
## Number of parameters (weights and biases) to estimate: 12
## Nguyen-Widrow method
## Scaling factor= 2.8
## gamma= 9.9796 alpha= 0.0004 beta= 54.2941
## brnn brnn gaussNewton i 5 summary statistics 3.5508 12.6079 2.9377 10.2283 time 0.08
## Number of parameters (weights and biases) to estimate: 12
## Nguyen-Widrow method
## Scaling factor= 2.8
## gamma= 11.4697 alpha= 0.0212 beta= 120.2874
## Number of parameters (weights and biases) to estimate: 12
## Nguyen-Widrow method
## Scaling factor= 2.8
## gamma= 10.0785 alpha= 0.0007 beta= 52.8564
## Number of parameters (weights and biases) to estimate: 12
## Nguyen-Widrow method
## Scaling factor= 2.8
## gamma= 10.0785 alpha= 0.0007 beta= 52.8559
## Number of parameters (weights and biases) to estimate: 12
## Nguyen-Widrow method
## Scaling factor= 2.8
## gamma= 11.4697 alpha= 0.0212 beta= 120.2874
## Number of parameters (weights and biases) to estimate: 12
## Nguyen-Widrow method
## Scaling factor= 2.8
## gamma= 11.4698 alpha= 0.0212 beta= 120.2876
## brnn brnn gaussNewton i 10 summary statistics 2.3781 5.6555 1.8657 7.3796 time 0.04
## Number of parameters (weights and biases) to estimate: 12
## Nguyen-Widrow method
## Scaling factor= 2.8
## gamma= 11.4697 alpha= 0.0212 beta= 120.2874
## Number of parameters (weights and biases) to estimate: 12
## Nguyen-Widrow method
## Scaling factor= 2.8
## gamma= 11.4698 alpha= 0.0212 beta= 120.2877
## Number of parameters (weights and biases) to estimate: 12
## Nguyen-Widrow method
## Scaling factor= 2.8
## gamma= 11.4698 alpha= 0.0212 beta= 120.2877
## Number of parameters (weights and biases) to estimate: 12
## Nguyen-Widrow method
## Scaling factor= 2.8
## gamma= 11.4698 alpha= 0.0212 beta= 120.2877
## Number of parameters (weights and biases) to estimate: 12
## Nguyen-Widrow method
## Scaling factor= 2.8
## gamma= 10.0785 alpha= 0.0007 beta= 52.8561
## brnn brnn gaussNewton i 15 summary statistics 3.598 12.9455 2.9794 10.3725 time 0.06
## Number of parameters (weights and biases) to estimate: 12
## Nguyen-Widrow method
## Scaling factor= 2.8
## gamma= 11.4698 alpha= 0.0212 beta= 120.2877
## Number of parameters (weights and biases) to estimate: 12
## Nguyen-Widrow method
## Scaling factor= 2.8
## gamma= 11.4698 alpha= 0.0212 beta= 120.2876
## Number of parameters (weights and biases) to estimate: 12
## Nguyen-Widrow method
## Scaling factor= 2.8
## gamma= 11.4697 alpha= 0.0212 beta= 120.2874
## Number of parameters (weights and biases) to estimate: 12
## Nguyen-Widrow method
## Scaling factor= 2.8
## gamma= 10.0785 alpha= 0.0007 beta= 52.857
## Number of parameters (weights and biases) to estimate: 12
## Nguyen-Widrow method
## Scaling factor= 2.8
## gamma= 11.4697 alpha= 0.0212 beta= 120.2874
## brnn brnn gaussNewton i 20 summary statistics 2.3781 5.6555 1.8657 7.3797 time 0.05
##
## ________________________________________________________________________________
## *** uGauss2_CaDENCE::cadence.fit_optim ***
## n.hidden = 4 --> 1 * NLL = -201.1487 ; penalty = 0; BIC = -302.911 ; AICc = -363.3363 ; AIC = -366.2973
## n.hidden = 4 --> 1 * NLL = -210.4412 ; penalty = 0; BIC = -321.4962 ; AICc = -381.9214 ; AIC = -384.8825
## n.hidden = 4 --> 1 * NLL = -215.317 ; penalty = 0; BIC = -331.2476 ; AICc = -391.6729 ; AIC = -394.6339
## n.hidden = 4 --> 1 * NLL = -285.3479 ; penalty = 0; BIC = -471.3095 ; AICc = -531.7348 ; AIC = -534.6958
## n.hidden = 4 --> 1 * NLL = -201.6434 ; penalty = 0; BIC = -303.9004 ; AICc = -364.3257 ; AIC = -367.2867
## CaDENCE cadence.fit optim i 5 summary statistics 8.5179 72.5542 5.0559 27.6055 time 2.31
## n.hidden = 4 --> 1 * NLL = -119.4165 ; penalty = 0; BIC = -139.4467 ; AICc = -199.8719 ; AIC = -202.833
## n.hidden = 4 --> 1 * NLL = -3.301943 ; penalty = 0; BIC = 92.78241 ; AICc = 32.35715 ; AIC = 29.39611
## n.hidden = 4 --> 1 * NLL = -95.37962 ; penalty = 0; BIC = -91.37294 ; AICc = -151.7982 ; AIC = -154.7592
## n.hidden = 4 --> 1 * NLL = -225.335 ; penalty = 0; BIC = -351.2837 ; AICc = -411.709 ; AIC = -414.67
## n.hidden = 4 --> 1 * NLL = -157.9433 ; penalty = 0; BIC = -216.5003 ; AICc = -276.9256 ; AIC = -279.8866
## CaDENCE cadence.fit optim i 10 summary statistics 16.7528 280.6555 8.506 58.7016 time 2.36
## n.hidden = 4 --> 1 * NLL = -94.9487 ; penalty = 0; BIC = -90.51111 ; AICc = -150.9364 ; AIC = -153.8974
## n.hidden = 4 --> 1 * NLL = -202.5622 ; penalty = 0; BIC = -305.7381 ; AICc = -366.1634 ; AIC = -369.1244
## n.hidden = 4 --> 1 * NLL = -95.76759 ; penalty = 0; BIC = -92.14888 ; AICc = -152.5741 ; AIC = -155.5352
## n.hidden = 4 --> 1 * NLL = -201.7466 ; penalty = 0; BIC = -304.107 ; AICc = -364.5322 ; AIC = -367.4933
## n.hidden = 4 --> 1 * NLL = -323.549 ; penalty = 0; BIC = -547.7117 ; AICc = -608.137 ; AIC = -611.098
## CaDENCE cadence.fit optim i 15 summary statistics 2.558 6.5435 2.0296 7.9177 time 2.3
## n.hidden = 4 --> 1 * NLL = -225.3429 ; penalty = 0; BIC = -351.2994 ; AICc = -411.7247 ; AIC = -414.6857
## n.hidden = 4 --> 1 * NLL = -95.23796 ; penalty = 0; BIC = -91.08963 ; AICc = -151.5149 ; AIC = -154.4759
## n.hidden = 4 --> 1 * NLL = -215.2447 ; penalty = 0; BIC = -331.103 ; AICc = -391.5283 ; AIC = -394.4893
## n.hidden = 4 --> 1 * NLL = -203.2343 ; penalty = 0; BIC = -307.0823 ; AICc = -367.5076 ; AIC = -370.4686
## n.hidden = 4 --> 1 * NLL = -58.97837 ; penalty = 0; BIC = -18.57044 ; AICc = -78.99569 ; AIC = -81.95673
## CaDENCE cadence.fit optim i 20 summary statistics 11.6319 135.3022 8.3228 28.3104 time 2.32
##
## ________________________________________________________________________________
## *** uGauss2_MachineShop::fit_none ***
## MachineShop fit none i 5 summary statistics 2.573 6.6203 2.0637 7.8422 time 0.02
## MachineShop fit none i 10 summary statistics 6.0789 36.9531 4.6167 17.2315 time 0.03
## MachineShop fit none i 15 summary statistics 2.3689 5.6117 1.8633 7.5461 time 0.04
## MachineShop fit none i 20 summary statistics 2.3644 5.5903 1.8598 7.5635 time 0.05
##
## ________________________________________________________________________________
## *** uGauss2_minpack.lm::nlsLM_none ***
## minpack.lm nlsLM none i 5 summary statistics 8.3794 70.2136 5.7627 29.2459 time 0.04
## minpack.lm nlsLM none i 10 summary statistics 4.9921 24.921 3.761 16.3421 time 0.05
## minpack.lm nlsLM none i 15 summary statistics 2.3335 5.4452 1.8313 7.324 time 0.03
## minpack.lm nlsLM none i 20 summary statistics 3.5011 12.2578 2.6021 13.3969 time 0.02
##
## ________________________________________________________________________________
## *** uGauss2_monmlp::monmlp.fit_BFGS ***
## monmlp monmlp.fit BFGS i 5 summary statistics 3.4563 11.9461 2.7565 10.6372 time 0.22
## monmlp monmlp.fit BFGS i 10 summary statistics 4.2114 17.7356 3.3037 12.8059 time 0.22
## monmlp monmlp.fit BFGS i 15 summary statistics 8.9611 80.3017 6.2984 30.143 time 0.22
## monmlp monmlp.fit BFGS i 20 summary statistics 8.0067 64.1066 5.7553 23.7322 time 0.21
##
## ________________________________________________________________________________
## *** uGauss2_nlsr::nlxb_none ***
## 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
## nlsr nlxb none i 5 summary statistics 8.0767 65.2323 5.1869 29.256 time 0.03
## 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
## nlsr nlxb none i 10 summary statistics 2.3338 5.4467 1.8312 7.3306 time 0.12
## 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
## nlsr nlxb none i 15 summary statistics 2.3334 5.4446 1.8308 7.3296 time 0.11
## 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
## nlsr nlxb none i 20 summary statistics 2.9751 8.8512 2.3831 9.0646 time 0.13
##
## ________________________________________________________________________________
## *** uGauss2_nnet::nnet_none ***
## nnet nnet none i 5 summary statistics 6.1248 37.5136 4.7261 17.0978 time 0.01
## nnet nnet none i 10 summary statistics 2.3782 5.6557 1.8702 7.63 time 0.02
## nnet nnet none i 15 summary statistics 6.0923 37.1162 4.5975 17.4169 time 0.02
## nnet nnet none i 20 summary statistics 3.0362 9.2185 2.4343 9.3329 time 0.01
##
## ________________________________________________________________________________
## *** uGauss2_qrnn::qrnn.fit_none ***
## qrnn qrnn.fit none i 5 summary statistics 2.3606 5.5722 1.8303 7.4027 time 0.27
## qrnn qrnn.fit none i 10 summary statistics 7.3064 53.3831 5.1111 23.2247 time 0.18
## qrnn qrnn.fit none i 15 summary statistics 3.4427 11.8521 2.5353 13.0345 time 0.44
## qrnn qrnn.fit none i 20 summary statistics 3.0506 9.3062 2.3269 9.4192 time 0.25
##
## ________________________________________________________________________________
## *** uGauss2_radiant.model::nn_none ***
## radiant.model nn none i 5 summary statistics 6.0884 37.0683 4.5903 17.4376 time 0.03
## radiant.model nn none i 10 summary statistics 5.6746 32.2012 4.3721 17.8157 time 0.05
## radiant.model nn none i 15 summary statistics 6.0948 37.1467 4.6004 17.4269 time 0.05
## radiant.model nn none i 20 summary statistics 2.5729 6.6199 2.035 7.5265 time 0.01
##
## ________________________________________________________________________________
## *** uGauss2_rminer::fit_none ***
## rminer fit none i 5 summary statistics 2.3865 5.6952 1.8754 7.6581 time 0.06
## rminer fit none i 10 summary statistics 2.3605 5.5719 1.8545 7.3104 time 0.08
## rminer fit none i 15 summary statistics 2.3556 5.5489 1.8512 7.4871 time 0.06
## rminer fit none i 20 summary statistics 3.0212 9.1275 2.4213 9.2651 time 0.06
##
## ________________________________________________________________________________
## *** uGauss2_validann::ann_BFGS ***
## initial value 324.829425
## iter 20 value 29.598254
## iter 40 value 10.622203
## iter 60 value 3.724587
## iter 80 value 1.610686
## iter 100 value 1.285815
## iter 120 value 1.217383
## iter 140 value 1.201998
## iter 160 value 1.193518
## iter 180 value 1.190466
## iter 200 value 1.184561
## final value 1.184561
## stopped after 200 iterations
## initial value 212.832587
## iter 20 value 46.885263
## iter 40 value 23.777075
## iter 60 value 16.183242
## iter 80 value 14.584524
## iter 100 value 13.077774
## iter 120 value 13.059297
## iter 140 value 11.971193
## iter 160 value 7.885890
## iter 180 value 7.154686
## iter 200 value 6.902793
## final value 6.902793
## stopped after 200 iterations
## initial value 246.260938
## iter 20 value 34.975289
## iter 40 value 7.985059
## iter 60 value 6.750122
## iter 80 value 6.534319
## iter 100 value 6.490787
## iter 120 value 6.476260
## iter 140 value 6.462148
## iter 160 value 6.456384
## iter 180 value 6.455206
## iter 200 value 6.451385
## final value 6.451385
## stopped after 200 iterations
## initial value 198.743445
## iter 20 value 28.007229
## iter 40 value 8.841008
## iter 60 value 4.828478
## iter 80 value 1.941632
## iter 100 value 1.146478
## iter 120 value 1.050593
## iter 140 value 0.996667
## iter 160 value 0.980357
## iter 180 value 0.975477
## iter 200 value 0.972067
## final value 0.972067
## stopped after 200 iterations
## initial value 266.914703
## iter 20 value 35.074463
## iter 40 value 15.884144
## iter 60 value 13.492452
## iter 80 value 9.326266
## iter 100 value 3.843196
## iter 120 value 2.016984
## iter 140 value 1.321507
## iter 160 value 1.137434
## iter 180 value 1.018935
## iter 200 value 0.994383
## final value 0.994383
## stopped after 200 iterations
## validann ann BFGS i 5 summary statistics 2.3816 5.6719 1.8796 7.5952 time 0.61
## initial value 415.541527
## iter 20 value 20.964060
## iter 40 value 15.378537
## iter 60 value 14.150762
## iter 80 value 13.072740
## iter 100 value 13.040966
## iter 120 value 12.760909
## iter 140 value 10.896577
## iter 160 value 6.172826
## iter 180 value 4.837629
## iter 200 value 4.573232
## final value 4.573232
## stopped after 200 iterations
## initial value 356.800140
## iter 20 value 50.878851
## iter 40 value 34.892568
## iter 60 value 12.609299
## iter 80 value 8.622357
## iter 100 value 7.149709
## iter 120 value 6.693163
## iter 140 value 6.627714
## iter 160 value 6.602103
## iter 180 value 6.577271
## iter 200 value 6.572975
## final value 6.572975
## stopped after 200 iterations
## initial value 466.278816
## iter 20 value 48.448569
## iter 40 value 15.680087
## iter 60 value 9.009146
## iter 80 value 2.876712
## iter 100 value 1.738705
## iter 120 value 1.286635
## iter 140 value 1.049471
## iter 160 value 0.994107
## iter 180 value 0.982975
## iter 200 value 0.976680
## final value 0.976680
## stopped after 200 iterations
## initial value 262.642889
## iter 20 value 14.242578
## iter 40 value 6.417513
## iter 60 value 4.323728
## iter 80 value 1.537529
## iter 100 value 1.232381
## iter 120 value 1.175381
## iter 140 value 1.057524
## iter 160 value 0.982275
## iter 180 value 0.976795
## iter 200 value 0.974621
## final value 0.974621
## stopped after 200 iterations
## initial value 246.219043
## iter 20 value 10.701241
## iter 40 value 4.057890
## iter 60 value 2.268882
## iter 80 value 1.412928
## iter 100 value 1.132758
## iter 120 value 1.010916
## iter 140 value 0.984922
## iter 160 value 0.974799
## iter 180 value 0.971911
## iter 200 value 0.970989
## final value 0.970989
## stopped after 200 iterations
## validann ann BFGS i 10 summary statistics 2.3534 5.5385 1.8458 7.4121 time 0.6
## initial value 256.427726
## iter 20 value 26.160153
## iter 40 value 9.842204
## iter 60 value 3.706787
## iter 80 value 1.334689
## iter 100 value 1.153265
## iter 120 value 1.073292
## iter 140 value 0.988693
## iter 160 value 0.973310
## iter 180 value 0.971702
## iter 200 value 0.969008
## final value 0.969008
## stopped after 200 iterations
## initial value 275.689642
## iter 20 value 20.583653
## iter 40 value 7.067579
## iter 60 value 2.826844
## iter 80 value 1.510433
## iter 100 value 1.273265
## iter 120 value 1.058009
## iter 140 value 0.999419
## iter 160 value 0.983781
## iter 180 value 0.979563
## iter 200 value 0.974942
## final value 0.974942
## stopped after 200 iterations
## initial value 330.666739
## iter 20 value 46.667002
## iter 40 value 20.997792
## iter 60 value 17.053704
## iter 80 value 15.856172
## iter 100 value 14.037155
## iter 120 value 12.776072
## iter 140 value 5.198744
## iter 160 value 1.984252
## iter 180 value 1.311633
## iter 200 value 1.110513
## final value 1.110513
## stopped after 200 iterations
## initial value 253.040145
## iter 20 value 43.732010
## iter 40 value 13.506854
## iter 60 value 11.231625
## iter 80 value 6.046115
## iter 100 value 2.522647
## iter 120 value 1.791998
## iter 140 value 1.689000
## iter 160 value 1.647313
## iter 180 value 1.605538
## iter 200 value 1.592091
## final value 1.592091
## stopped after 200 iterations
## initial value 263.940949
## iter 20 value 56.178329
## iter 40 value 49.237473
## iter 60 value 31.726922
## iter 80 value 11.023052
## iter 100 value 5.450522
## iter 120 value 3.750771
## iter 140 value 3.451572
## iter 160 value 3.318125
## iter 180 value 3.275985
## iter 200 value 3.242404
## final value 3.242404
## stopped after 200 iterations
## validann ann BFGS i 15 summary statistics 4.3005 18.4945 3.3821 11.9728 time 0.59
## initial value 241.243832
## iter 20 value 50.333965
## iter 40 value 8.375688
## iter 60 value 1.797659
## iter 80 value 1.411097
## iter 100 value 1.128425
## iter 120 value 1.013849
## iter 140 value 0.987176
## iter 160 value 0.980431
## iter 180 value 0.975063
## iter 200 value 0.972963
## final value 0.972963
## stopped after 200 iterations
## initial value 238.117964
## iter 20 value 31.398600
## iter 40 value 15.355227
## iter 60 value 13.845798
## iter 80 value 12.276643
## iter 100 value 12.057551
## iter 120 value 11.887696
## iter 140 value 11.826448
## iter 160 value 11.793072
## iter 180 value 11.766026
## iter 200 value 11.747368
## final value 11.747368
## stopped after 200 iterations
## initial value 322.320482
## iter 20 value 42.634901
## iter 40 value 14.767567
## iter 60 value 13.173273
## iter 80 value 13.068213
## iter 100 value 13.067252
## iter 120 value 13.064917
## iter 140 value 13.060202
## iter 160 value 13.050746
## iter 180 value 13.032880
## iter 200 value 13.015081
## final value 13.015081
## stopped after 200 iterations
## initial value 209.655109
## iter 20 value 46.477635
## iter 40 value 13.938457
## iter 60 value 6.706810
## iter 80 value 3.905298
## iter 100 value 1.440128
## iter 120 value 1.137294
## iter 140 value 1.108334
## iter 160 value 1.065707
## iter 180 value 1.007537
## iter 200 value 0.976869
## final value 0.976869
## stopped after 200 iterations
## initial value 234.507352
## iter 20 value 46.615549
## iter 40 value 25.637800
## iter 60 value 13.029030
## iter 80 value 4.941779
## iter 100 value 2.010981
## iter 120 value 1.537658
## iter 140 value 1.137054
## iter 160 value 1.014961
## iter 180 value 0.994788
## iter 200 value 0.980824
## final value 0.980824
## stopped after 200 iterations
## validann ann BFGS i 20 summary statistics 2.3653 5.5946 1.8554 7.4963 time 0.61
##
## ________________________________________________________________________________
## *** uGauss2_validann::ann_L-BFGS-B ***
## iter 20 value 43.057937
## iter 40 value 18.117254
## iter 60 value 16.163929
## iter 80 value 15.890409
## iter 100 value 15.234861
## iter 120 value 15.013666
## iter 140 value 14.947667
## iter 160 value 14.915003
## iter 180 value 14.894328
## iter 200 value 14.870977
## final value 14.870666
## stopped after 201 iterations
## iter 20 value 42.915716
## iter 40 value 16.662020
## iter 60 value 12.675119
## iter 80 value 11.367272
## iter 100 value 7.483664
## iter 120 value 4.726614
## iter 140 value 3.298756
## iter 160 value 3.137838
## iter 180 value 2.992441
## iter 200 value 2.590210
## final value 2.558589
## stopped after 201 iterations
## iter 20 value 38.588256
## iter 40 value 19.588149
## iter 60 value 16.892287
## iter 80 value 13.685472
## iter 100 value 13.225537
## iter 120 value 13.098994
## iter 140 value 12.845434
## iter 160 value 12.774692
## iter 180 value 12.331142
## iter 200 value 12.134520
## final value 12.130327
## stopped after 201 iterations
## iter 20 value 38.704089
## iter 40 value 14.771787
## iter 60 value 10.648697
## iter 80 value 6.558048
## iter 100 value 4.078989
## iter 120 value 2.622566
## iter 140 value 2.553836
## iter 160 value 2.460728
## iter 180 value 2.370304
## iter 200 value 2.326806
## final value 2.321327
## stopped after 201 iterations
## iter 20 value 40.232369
## iter 40 value 15.150204
## iter 60 value 14.437450
## iter 80 value 13.329556
## iter 100 value 10.002460
## iter 120 value 8.465352
## iter 140 value 7.000035
## iter 160 value 5.215373
## iter 180 value 3.420121
## iter 200 value 2.680395
## final value 2.665047
## stopped after 201 iterations
## validann ann L-BFGS-B i 5 summary statistics 3.8989 15.2013 3.1868 12.0111 time 0.62
## iter 20 value 40.322653
## iter 40 value 8.972496
## iter 60 value 5.709593
## iter 80 value 3.586006
## iter 100 value 2.704373
## iter 120 value 2.542096
## iter 140 value 2.434385
## iter 160 value 1.779285
## iter 180 value 1.726708
## iter 200 value 1.707350
## final value 1.705226
## stopped after 201 iterations
## iter 20 value 42.064527
## iter 40 value 18.660986
## iter 60 value 14.868956
## iter 80 value 14.335485
## iter 100 value 9.058832
## iter 120 value 6.017571
## iter 140 value 5.148442
## iter 160 value 5.035678
## iter 180 value 4.979631
## iter 200 value 4.700740
## final value 4.700364
## stopped after 201 iterations
## iter 20 value 44.163328
## iter 40 value 13.002985
## iter 60 value 8.313654
## iter 80 value 3.671843
## iter 100 value 3.045114
## iter 120 value 2.727250
## iter 140 value 1.829820
## iter 160 value 1.755243
## iter 180 value 1.594114
## iter 200 value 1.339336
## final value 1.334550
## stopped after 201 iterations
## iter 20 value 45.545097
## iter 40 value 25.657187
## iter 60 value 15.737780
## iter 80 value 14.400287
## iter 100 value 12.187417
## iter 120 value 11.837683
## iter 140 value 11.413894
## iter 160 value 11.118794
## iter 180 value 11.064283
## iter 200 value 11.033174
## final value 11.032981
## stopped after 201 iterations
## iter 20 value 42.049288
## iter 40 value 8.247870
## iter 60 value 7.700854
## iter 80 value 7.475627
## iter 100 value 7.164168
## iter 120 value 6.899059
## iter 140 value 6.862113
## iter 160 value 6.859171
## iter 180 value 6.854535
## iter 200 value 6.842768
## final value 6.841487
## stopped after 201 iterations
## validann ann L-BFGS-B i 10 summary statistics 6.2469 39.0235 4.8579 17.5935 time 0.62
## iter 20 value 41.539689
## iter 40 value 24.109572
## iter 60 value 22.857432
## iter 80 value 16.395670
## iter 100 value 15.752413
## iter 120 value 15.546186
## iter 140 value 15.541286
## iter 160 value 15.539457
## iter 180 value 15.538363
## iter 200 value 15.536419
## final value 15.536400
## stopped after 201 iterations
## iter 20 value 40.026117
## iter 40 value 15.953400
## iter 60 value 14.787311
## iter 80 value 13.026614
## iter 100 value 9.115326
## iter 120 value 6.331767
## iter 140 value 4.246976
## iter 160 value 3.803419
## iter 180 value 3.538423
## iter 200 value 3.192387
## final value 3.162423
## stopped after 201 iterations
## iter 20 value 43.482652
## iter 40 value 20.182334
## iter 60 value 15.902623
## iter 80 value 15.521578
## iter 100 value 14.641657
## iter 120 value 14.268848
## iter 140 value 13.295245
## iter 160 value 12.839710
## iter 180 value 11.543482
## iter 200 value 8.368787
## final value 8.324851
## stopped after 201 iterations
## iter 20 value 33.199069
## iter 40 value 10.135230
## iter 60 value 8.771097
## iter 80 value 6.742239
## iter 100 value 5.641139
## iter 120 value 3.695178
## iter 140 value 3.013407
## iter 160 value 2.876257
## iter 180 value 2.691902
## iter 200 value 2.663565
## final value 2.663122
## stopped after 201 iterations
## iter 20 value 41.617182
## iter 40 value 16.029741
## iter 60 value 15.311229
## iter 80 value 14.239646
## iter 100 value 10.887147
## iter 120 value 7.296250
## iter 140 value 6.868614
## iter 160 value 6.128712
## iter 180 value 5.740254
## iter 200 value 4.250063
## final value 4.191760
## stopped after 201 iterations
## validann ann L-BFGS-B i 15 summary statistics 4.8897 23.9096 3.5916 17.2923 time 0.7
## iter 20 value 33.178082
## iter 40 value 15.463122
## iter 60 value 12.543857
## iter 80 value 12.283414
## iter 100 value 11.306184
## iter 120 value 10.177734
## iter 140 value 9.680209
## iter 160 value 9.298498
## iter 180 value 8.810808
## iter 200 value 7.622173
## final value 7.584274
## stopped after 201 iterations
## iter 20 value 40.477636
## iter 40 value 20.543335
## iter 60 value 14.884064
## iter 80 value 12.899620
## iter 100 value 11.968652
## iter 120 value 11.582795
## iter 140 value 11.127917
## iter 160 value 10.718885
## iter 180 value 9.939435
## iter 200 value 9.407860
## final value 9.400320
## stopped after 201 iterations
## iter 20 value 43.527537
## iter 40 value 18.991569
## iter 60 value 12.150314
## iter 80 value 10.382333
## iter 100 value 9.325120
## iter 120 value 8.218696
## iter 140 value 5.354164
## iter 160 value 4.127846
## iter 180 value 3.616179
## iter 200 value 2.654636
## final value 2.644240
## stopped after 201 iterations
## iter 20 value 14.225083
## iter 40 value 7.360182
## iter 60 value 7.287383
## iter 80 value 7.084397
## iter 100 value 6.953985
## iter 120 value 6.903463
## iter 140 value 6.773586
## iter 160 value 6.712073
## iter 180 value 6.689453
## iter 200 value 6.647728
## final value 6.643635
## stopped after 201 iterations
## iter 20 value 41.492489
## iter 40 value 10.861627
## iter 60 value 9.018355
## iter 80 value 8.073916
## iter 100 value 7.106534
## iter 120 value 6.634428
## iter 140 value 6.001369
## iter 160 value 5.699669
## iter 180 value 5.439581
## iter 200 value 3.955401
## final value 3.894215
## stopped after 201 iterations
## validann ann L-BFGS-B i 20 summary statistics 4.713 22.2124 3.6944 12.4547 time 0.67
##
## ________________________________________________________________________________
## *** uGauss3_brnn::brnn_gaussNewton ***
## Number of parameters (weights and biases) to estimate: 12
## Nguyen-Widrow method
## Scaling factor= 2.8
## gamma= 11.69 alpha= 0.0483 beta= 75.1743
## 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= 11.8754 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
## brnn brnn gaussNewton i 5 summary statistics 3.1966 10.2185 2.5109 10.0156 time 0.03
## Number of parameters (weights and biases) to estimate: 12
## Nguyen-Widrow method
## Scaling factor= 2.8
## gamma= 11.4151 alpha= 0.053 beta= 132.0234
## Number of parameters (weights and biases) to estimate: 12
## Nguyen-Widrow method
## Scaling factor= 2.8
## gamma= 11.6896 alpha= 0.0483 beta= 75.177
## 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= 10.4245 alpha= 0.009 beta= 5.2048
## Number of parameters (weights and biases) to estimate: 12
## Nguyen-Widrow method
## Scaling factor= 2.8
## gamma= 11.1368 alpha= 0.0547 beta= 123.871
## brnn brnn gaussNewton i 10 summary statistics 2.4932 6.216 1.9675 7.4445 time 0.03
## 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= 11.1022 alpha= 0.0534 beta= 124.0395
## 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= 11.1847 alpha= 0.0471 beta= 63.6557
## 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
## brnn brnn gaussNewton i 15 summary statistics 2.8273 7.9934 2.2154 7.6585 time 0.03
## 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= 11.1022 alpha= 0.0534 beta= 124.0394
## Number of parameters (weights and biases) to estimate: 12
## Nguyen-Widrow method
## Scaling factor= 2.8
## gamma= 11.69 alpha= 0.0483 beta= 75.1743
## Number of parameters (weights and biases) to estimate: 12
## Nguyen-Widrow method
## Scaling factor= 2.8
## gamma= 11.69 alpha= 0.0483 beta= 75.1743
## Number of parameters (weights and biases) to estimate: 12
## Nguyen-Widrow method
## Scaling factor= 2.8
## gamma= 11.1022 alpha= 0.0534 beta= 124.0395
## brnn brnn gaussNewton i 20 summary statistics 2.4917 6.2084 1.9659 7.4363 time 0.02
##
## ________________________________________________________________________________
## *** uGauss3_CaDENCE::cadence.fit_optim ***
## n.hidden = 4 --> 1 * NLL = -306.0493 ; penalty = 0; BIC = -512.7123 ; AICc = -573.1376 ; AIC = -576.0986
## n.hidden = 4 --> 1 * NLL = -297.6341 ; penalty = 0; BIC = -495.882 ; AICc = -556.3072 ; AIC = -559.2682
## n.hidden = 4 --> 1 * NLL = -294.7149 ; penalty = 0; BIC = -490.0434 ; AICc = -550.4687 ; AIC = -553.4297
## n.hidden = 4 --> 1 * NLL = -296.3637 ; penalty = 0; BIC = -493.3411 ; AICc = -553.7664 ; AIC = -556.7274
## n.hidden = 4 --> 1 * NLL = -298.7828 ; penalty = 0; BIC = -498.1794 ; AICc = -558.6046 ; AIC = -561.5656
## CaDENCE cadence.fit optim i 5 summary statistics 3.7906 14.3688 2.7099 13.0438 time 2.47
## n.hidden = 4 --> 1 * NLL = -301.1142 ; penalty = 0; BIC = -502.842 ; AICc = -563.2673 ; AIC = -566.2283
## n.hidden = 4 --> 1 * NLL = -299.1416 ; penalty = 0; BIC = -498.8969 ; AICc = -559.3222 ; AIC = -562.2832
## n.hidden = 4 --> 1 * NLL = -362.4188 ; penalty = 0; BIC = -625.4513 ; AICc = -685.8765 ; AIC = -688.8376
## n.hidden = 4 --> 1 * NLL = -243.6122 ; penalty = 0; BIC = -387.838 ; AICc = -448.2633 ; AIC = -451.2243
## n.hidden = 4 --> 1 * NLL = -302.5014 ; penalty = 0; BIC = -505.6165 ; AICc = -566.0418 ; AIC = -569.0028
## CaDENCE cadence.fit optim i 10 summary statistics 3.3302 11.0904 2.4951 11.2009 time 2.35
## n.hidden = 4 --> 1 * NLL = -301.1935 ; penalty = 0; BIC = -503.0008 ; AICc = -563.426 ; AIC = -566.3871
## n.hidden = 4 --> 1 * NLL = -298.9512 ; penalty = 0; BIC = -498.5161 ; AICc = -558.9414 ; AIC = -561.9024
## n.hidden = 4 --> 1 * NLL = -298.6397 ; penalty = 0; BIC = -497.893 ; AICc = -558.3183 ; AIC = -561.2793
## n.hidden = 4 --> 1 * NLL = -363.0472 ; penalty = 0; BIC = -626.7082 ; AICc = -687.1334 ; AIC = -690.0945
## n.hidden = 4 --> 1 * NLL = -206.7468 ; penalty = 0; BIC = -314.1074 ; AICc = -374.5326 ; AIC = -377.4937
## CaDENCE cadence.fit optim i 15 summary statistics 9.1071 82.9395 5.5921 27.6727 time 2.43
## n.hidden = 4 --> 1 * NLL = -300.7381 ; penalty = 0; BIC = -502.0898 ; AICc = -562.5151 ; AIC = -565.4761
## n.hidden = 4 --> 1 * NLL = -222.3034 ; penalty = 0; BIC = -345.2204 ; AICc = -405.6457 ; AIC = -408.6067
## n.hidden = 4 --> 1 * NLL = -300.2714 ; penalty = 0; BIC = -501.1564 ; AICc = -561.5817 ; AIC = -564.5427
## n.hidden = 4 --> 1 * NLL = -299.1091 ; penalty = 0; BIC = -498.8318 ; AICc = -559.2571 ; AIC = -562.2181
## n.hidden = 4 --> 1 * NLL = -224.6793 ; penalty = 0; BIC = -349.9723 ; AICc = -410.3976 ; AIC = -413.3586
## CaDENCE cadence.fit optim i 20 summary statistics 8.2111 67.4217 4.9936 28.9438 time 2.35
##
## ________________________________________________________________________________
## *** uGauss3_MachineShop::fit_none ***
## MachineShop fit none i 5 summary statistics 2.7032 7.3072 2.1412 9.0729 time 0.04
## MachineShop fit none i 10 summary statistics 3.2141 10.3302 2.5391 9.9349 time 0.03
## MachineShop fit none i 15 summary statistics 3.4955 12.2188 2.8095 10.7277 time 0.03
## MachineShop fit none i 20 summary statistics 2.3163 5.3653 1.8561 6.3187 time 0.03
##
## ________________________________________________________________________________
## *** uGauss3_minpack.lm::nlsLM_none ***
## minpack.lm nlsLM none i 5 summary statistics 3.1487 9.9143 2.485 9.7353 time 0.04
## minpack.lm nlsLM none i 10 summary statistics 40.0663 1605.307 34.1007 74.5959 time 0.02
## minpack.lm nlsLM none i 15 summary statistics 2.8185 7.9439 2.2078 7.5078 time 0.01
## minpack.lm nlsLM none i 20 summary statistics 2.3381 5.4667 1.8436 7.1469 time 0.04
##
## ________________________________________________________________________________
## *** uGauss3_monmlp::monmlp.fit_BFGS ***
## monmlp monmlp.fit BFGS i 5 summary statistics 3.4706 12.0453 2.6798 10.3036 time 0.22
## monmlp monmlp.fit BFGS i 10 summary statistics 2.9613 8.769 2.2975 8.2438 time 0.22
## monmlp monmlp.fit BFGS i 15 summary statistics 2.6894 7.2328 2.1632 7.7449 time 0.22
## monmlp monmlp.fit BFGS i 20 summary statistics 3.5226 12.4084 2.7949 10.6376 time 0.21
##
## ________________________________________________________________________________
## *** uGauss3_nlsr::nlxb_none ***
## 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
## nlsr nlxb none i 5 summary statistics 3.4955 12.2185 2.8095 10.7276 time 0.11
## 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
## nlsr nlxb none i 10 summary statistics 2.2992 5.2863 1.8378 7.0765 time 0.11
## 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
## nlsr nlxb none i 15 summary statistics 2.2992 5.2863 1.8378 7.0765 time 0.11
## 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
## nlsr nlxb none i 20 summary statistics 2.8185 7.9439 2.2078 7.5077 time 0.03
##
## ________________________________________________________________________________
## *** uGauss3_nnet::nnet_none ***
## nnet nnet none i 5 summary statistics 2.292 5.2532 1.8266 6.3332 time 0.03
## nnet nnet none i 10 summary statistics 5.891 34.7034 4.4835 17.2501 time 0.03
## nnet nnet none i 15 summary statistics 3.3937 11.5175 2.5911 10.4298 time 0.01
## nnet nnet none i 20 summary statistics 2.299 5.2853 1.8317 6.3833 time 0.01
##
## ________________________________________________________________________________
## *** uGauss3_qrnn::qrnn.fit_none ***
## qrnn qrnn.fit none i 5 summary statistics 3.6463 13.2956 2.6142 13.7344 time 0.18
## qrnn qrnn.fit none i 10 summary statistics 3.2735 10.7158 2.3984 12.1406 time 0.22
## qrnn qrnn.fit none i 15 summary statistics 3.6964 13.6636 2.6197 14.0701 time 0.22
## qrnn qrnn.fit none i 20 summary statistics 2.9121 8.4803 2.2104 7.9176 time 0.27
##
## ________________________________________________________________________________
## *** uGauss3_radiant.model::nn_none ***
## radiant.model nn none i 5 summary statistics 3.1011 9.6168 2.4724 10.0738 time 0.04
## radiant.model nn none i 10 summary statistics 3.6034 12.9847 2.6711 12.126 time 0.01
## radiant.model nn none i 15 summary statistics 3.0692 9.4198 2.4326 9.8964 time 0.05
## radiant.model nn none i 20 summary statistics 3.2437 10.5218 2.565 9.999 time 0.05
##
## ________________________________________________________________________________
## *** uGauss3_rminer::fit_none ***
## rminer fit none i 5 summary statistics 3.157 9.9666 2.4935 9.7547 time 0.06
## rminer fit none i 10 summary statistics 2.3563 5.5523 1.86 7.0877 time 0.07
## rminer fit none i 15 summary statistics 2.2886 5.2376 1.8202 6.4225 time 0.09
## rminer fit none i 20 summary statistics 2.3072 5.323 1.8524 6.949 time 0.06
##
## ________________________________________________________________________________
## *** uGauss3_validann::ann_BFGS ***
## initial value 245.058151
## iter 20 value 4.946432
## iter 40 value 2.199908
## iter 60 value 2.081853
## iter 80 value 1.987066
## iter 100 value 1.904095
## iter 120 value 1.797997
## iter 140 value 1.732012
## iter 160 value 1.683708
## iter 180 value 1.650781
## iter 200 value 1.625075
## final value 1.625075
## stopped after 200 iterations
## initial value 277.954343
## iter 20 value 14.066771
## iter 40 value 2.064592
## iter 60 value 1.974797
## iter 80 value 1.893526
## iter 100 value 1.888769
## iter 120 value 1.886846
## iter 140 value 1.882566
## iter 160 value 1.877744
## iter 180 value 1.873255
## iter 200 value 1.868853
## final value 1.868853
## stopped after 200 iterations
## initial value 276.231433
## iter 20 value 7.358772
## iter 40 value 2.271410
## iter 60 value 1.973908
## iter 80 value 1.349026
## iter 100 value 1.271457
## iter 120 value 1.247078
## iter 140 value 1.232276
## iter 160 value 1.232190
## final value 1.232190
## converged
## initial value 310.747395
## iter 20 value 47.032092
## iter 40 value 15.307145
## iter 60 value 9.148105
## iter 80 value 2.926767
## iter 100 value 1.816046
## iter 120 value 1.803941
## iter 140 value 1.794300
## iter 160 value 1.713937
## iter 180 value 1.586206
## iter 200 value 1.571685
## final value 1.571685
## stopped after 200 iterations
## initial value 263.794656
## iter 20 value 5.830564
## iter 40 value 1.943788
## iter 60 value 1.397231
## iter 80 value 1.273981
## iter 100 value 1.267175
## iter 120 value 1.249493
## iter 140 value 1.233026
## final value 1.232190
## converged
## validann ann BFGS i 5 summary statistics 2.8185 7.9439 2.2078 7.5076 time 0.48
## initial value 208.147644
## iter 20 value 3.255153
## iter 40 value 1.321000
## iter 60 value 1.000520
## iter 80 value 0.900705
## iter 100 value 0.825886
## iter 120 value 0.820964
## iter 140 value 0.815148
## iter 160 value 0.811663
## iter 180 value 0.810152
## iter 200 value 0.809684
## final value 0.809684
## stopped after 200 iterations
## initial value 246.985071
## iter 20 value 16.779665
## iter 40 value 2.316732
## iter 60 value 1.899942
## iter 80 value 1.892797
## iter 100 value 1.891376
## iter 120 value 1.888602
## iter 140 value 1.884927
## iter 160 value 1.842791
## iter 180 value 1.737593
## iter 200 value 1.693953
## final value 1.693953
## stopped after 200 iterations
## initial value 267.192510
## iter 20 value 25.295080
## iter 40 value 5.563491
## iter 60 value 1.915186
## iter 80 value 1.892285
## iter 100 value 1.890569
## iter 120 value 1.887361
## iter 140 value 1.884001
## iter 160 value 1.880009
## iter 180 value 1.874691
## iter 200 value 1.859817
## final value 1.859817
## stopped after 200 iterations
## initial value 251.502389
## iter 20 value 3.796240
## iter 40 value 2.162635
## iter 60 value 1.968785
## iter 80 value 1.384606
## iter 100 value 1.238988
## iter 120 value 1.234455
## iter 140 value 1.232190
## final value 1.232190
## converged
## initial value 295.312020
## iter 20 value 10.438365
## iter 40 value 2.354983
## iter 60 value 2.172873
## iter 80 value 2.077983
## iter 100 value 2.013742
## iter 120 value 2.009108
## iter 140 value 2.007407
## iter 160 value 2.004833
## iter 180 value 1.998834
## iter 200 value 1.985288
## final value 1.985288
## stopped after 200 iterations
## validann ann BFGS i 10 summary statistics 3.5776 12.7992 2.7483 11.4536 time 0.59
## initial value 197.097296
## iter 20 value 12.634685
## iter 40 value 2.257414
## iter 60 value 1.903958
## iter 80 value 1.895245
## iter 80 value 1.895245
## iter 80 value 1.895245
## final value 1.895245
## converged
## initial value 367.327814
## iter 20 value 17.453863
## iter 40 value 10.010769
## iter 60 value 2.145407
## iter 80 value 1.267292
## iter 100 value 1.190924
## iter 120 value 1.157944
## iter 140 value 1.130144
## iter 160 value 1.124091
## iter 180 value 1.118235
## iter 200 value 1.109466
## final value 1.109466
## stopped after 200 iterations
## initial value 344.123693
## iter 20 value 24.390774
## iter 40 value 10.619894
## iter 60 value 2.193259
## iter 80 value 1.950581
## iter 100 value 1.427482
## iter 120 value 1.253562
## iter 140 value 1.232994
## iter 160 value 1.232201
## final value 1.232190
## converged
## initial value 277.298490
## iter 20 value 24.249660
## iter 40 value 9.816279
## iter 60 value 4.970716
## iter 80 value 2.139951
## iter 100 value 1.954984
## iter 120 value 1.825223
## iter 140 value 1.708433
## iter 160 value 1.609430
## iter 180 value 1.576117
## iter 200 value 1.562798
## final value 1.562798
## stopped after 200 iterations
## initial value 192.554930
## iter 20 value 12.146925
## iter 40 value 5.184573
## iter 60 value 2.220982
## iter 80 value 1.862482
## iter 100 value 1.797884
## iter 120 value 1.736091
## iter 140 value 1.668008
## iter 160 value 1.630607
## iter 180 value 1.597329
## iter 200 value 1.555983
## final value 1.555983
## stopped after 200 iterations
## validann ann BFGS i 15 summary statistics 3.1672 10.0314 2.4946 9.7112 time 0.59
## initial value 220.884942
## iter 20 value 23.066458
## iter 40 value 4.740968
## iter 60 value 2.647397
## iter 80 value 2.057946
## iter 100 value 1.990773
## iter 120 value 1.871807
## iter 140 value 1.786814
## iter 160 value 1.770030
## iter 180 value 1.764549
## iter 200 value 1.756122
## final value 1.756122
## stopped after 200 iterations
## initial value 242.953823
## iter 20 value 16.414144
## iter 40 value 2.366974
## iter 60 value 2.044298
## iter 80 value 2.001376
## iter 100 value 1.992136
## iter 120 value 1.918042
## final value 1.895278
## converged
## initial value 233.841834
## iter 20 value 24.164937
## iter 40 value 8.308543
## iter 60 value 5.785393
## iter 80 value 3.034858
## iter 100 value 1.251139
## iter 120 value 1.081651
## iter 140 value 0.940019
## iter 160 value 0.861083
## iter 180 value 0.821503
## iter 200 value 0.808665
## final value 0.808665
## stopped after 200 iterations
## initial value 283.267466
## iter 20 value 16.307250
## iter 40 value 2.096250
## iter 60 value 1.919163
## iter 80 value 1.893778
## iter 100 value 1.893061
## iter 120 value 1.891290
## iter 140 value 1.888970
## iter 160 value 1.885457
## iter 180 value 1.879576
## iter 200 value 1.847399
## final value 1.847399
## stopped after 200 iterations
## initial value 259.109493
## iter 20 value 8.106120
## iter 40 value 2.576940
## iter 60 value 2.123644
## iter 80 value 1.439362
## iter 100 value 1.301829
## iter 120 value 1.281350
## iter 140 value 1.279610
## iter 160 value 1.278949
## iter 180 value 1.276876
## iter 200 value 1.270940
## final value 1.270940
## stopped after 200 iterations
## validann ann BFGS i 20 summary statistics 2.8625 8.1938 2.2407 7.8989 time 0.59
##
## ________________________________________________________________________________
## *** uGauss3_validann::ann_L-BFGS-B ***
## iter 20 value 28.472367
## iter 40 value 3.908189
## iter 60 value 2.126961
## iter 80 value 1.824286
## iter 100 value 1.776281
## iter 120 value 1.727182
## iter 140 value 1.699022
## iter 160 value 1.692598
## iter 180 value 1.685660
## iter 200 value 1.675207
## final value 1.674642
## stopped after 201 iterations
## iter 20 value 27.172768
## iter 40 value 10.172670
## iter 60 value 4.727043
## iter 80 value 3.939822
## iter 100 value 3.107234
## iter 120 value 2.294129
## iter 140 value 2.236250
## iter 160 value 2.219842
## iter 180 value 2.202786
## iter 200 value 2.155816
## final value 2.153561
## stopped after 201 iterations
## iter 20 value 24.940018
## iter 40 value 2.526191
## iter 60 value 2.015845
## iter 80 value 1.754181
## iter 100 value 1.702545
## iter 120 value 1.695949
## iter 140 value 1.689476
## iter 160 value 1.685734
## iter 180 value 1.683563
## iter 200 value 1.680414
## final value 1.680313
## stopped after 201 iterations
## iter 20 value 25.875777
## iter 40 value 6.151902
## iter 60 value 2.296268
## iter 80 value 1.379483
## iter 100 value 1.276897
## iter 120 value 1.253767
## iter 140 value 1.109006
## iter 160 value 1.064923
## iter 180 value 1.060809
## iter 200 value 1.029858
## final value 1.027831
## stopped after 201 iterations
## iter 20 value 23.409494
## iter 40 value 3.310439
## iter 60 value 2.230303
## iter 80 value 2.031324
## iter 100 value 1.620135
## iter 120 value 1.313522
## iter 140 value 1.308548
## iter 160 value 1.289068
## iter 180 value 1.268170
## iter 200 value 1.264520
## final value 1.264470
## stopped after 201 iterations
## validann ann L-BFGS-B i 5 summary statistics 2.8552 8.1521 2.2406 7.7305 time 0.62
## iter 20 value 26.948669
## iter 40 value 14.115792
## iter 60 value 3.065797
## iter 80 value 2.701881
## iter 100 value 2.564927
## iter 120 value 2.058071
## iter 140 value 2.038039
## iter 160 value 2.012039
## iter 180 value 1.987671
## iter 200 value 1.953995
## final value 1.947637
## stopped after 201 iterations
## iter 20 value 23.254502
## iter 40 value 3.965632
## iter 60 value 2.701965
## iter 80 value 2.476007
## iter 100 value 1.960193
## iter 120 value 1.809055
## iter 140 value 1.795240
## iter 160 value 1.794791
## iter 180 value 1.793531
## iter 200 value 1.791547
## final value 1.791425
## stopped after 201 iterations
## iter 20 value 24.840491
## iter 40 value 2.075187
## iter 60 value 2.035998
## iter 80 value 1.976755
## iter 100 value 1.957015
## iter 120 value 1.943660
## iter 140 value 1.928370
## iter 160 value 1.925210
## iter 180 value 1.919173
## iter 200 value 1.905624
## final value 1.905599
## stopped after 201 iterations
## iter 20 value 21.425489
## iter 40 value 1.138329
## iter 60 value 1.021285
## iter 80 value 0.986070
## iter 100 value 0.966680
## iter 120 value 0.926302
## iter 140 value 0.875823
## iter 160 value 0.854880
## iter 180 value 0.849225
## iter 200 value 0.844989
## final value 0.844818
## stopped after 201 iterations
## iter 20 value 28.629728
## iter 40 value 16.920131
## iter 60 value 6.185220
## iter 80 value 2.272433
## iter 100 value 2.270016
## iter 120 value 2.269109
## iter 140 value 2.267771
## iter 160 value 2.267461
## iter 180 value 2.267426
## iter 200 value 2.267117
## final value 2.267104
## stopped after 201 iterations
## validann ann L-BFGS-B i 10 summary statistics 3.8231 14.6161 2.8628 11.98 time 0.69
## iter 20 value 26.690385
## iter 40 value 1.736020
## iter 60 value 1.361073
## iter 80 value 1.123334
## iter 100 value 1.090463
## iter 120 value 1.056209
## iter 140 value 1.034992
## iter 160 value 0.963815
## iter 180 value 0.855442
## iter 200 value 0.846876
## final value 0.846800
## stopped after 201 iterations
## iter 20 value 22.045135
## iter 40 value 2.632349
## iter 60 value 2.112060
## iter 80 value 2.037687
## iter 100 value 2.020796
## iter 120 value 2.014370
## iter 140 value 2.002222
## iter 160 value 1.998199
## iter 180 value 1.995806
## iter 200 value 1.990570
## final value 1.990362
## stopped after 201 iterations
## iter 20 value 20.057461
## iter 40 value 4.623141
## iter 60 value 1.859400
## iter 80 value 1.432250
## iter 100 value 1.394271
## iter 120 value 1.354165
## iter 140 value 1.206842
## iter 160 value 1.118799
## iter 180 value 1.092014
## iter 200 value 1.007179
## final value 1.005067
## stopped after 201 iterations
## iter 20 value 26.586383
## iter 40 value 12.226023
## iter 60 value 2.963414
## iter 80 value 2.310296
## iter 100 value 2.241259
## iter 120 value 2.218958
## iter 140 value 2.164386
## iter 160 value 2.118551
## iter 180 value 2.048576
## iter 200 value 2.006168
## final value 2.005879
## stopped after 201 iterations
## iter 20 value 25.026607
## iter 40 value 2.595400
## iter 60 value 2.126023
## iter 80 value 2.000473
## iter 100 value 1.992214
## iter 120 value 1.986433
## iter 140 value 1.983455
## iter 160 value 1.977594
## iter 180 value 1.974941
## iter 200 value 1.967205
## final value 1.967085
## stopped after 201 iterations
## validann ann L-BFGS-B i 15 summary statistics 3.5612 12.6818 2.7658 11.0553 time 0.66
## iter 20 value 13.463404
## iter 40 value 2.174564
## iter 60 value 1.917606
## iter 80 value 1.901949
## iter 100 value 1.900729
## iter 120 value 1.897503
## iter 140 value 1.896782
## iter 160 value 1.896584
## iter 180 value 1.896276
## iter 200 value 1.896118
## final value 1.896099
## stopped after 201 iterations
## iter 20 value 27.644551
## iter 40 value 3.142127
## iter 60 value 2.776129
## iter 80 value 2.117370
## iter 100 value 1.998453
## iter 120 value 1.971159
## iter 140 value 1.964392
## iter 160 value 1.956334
## iter 180 value 1.949565
## iter 200 value 1.942733
## final value 1.942335
## stopped after 201 iterations
## iter 20 value 11.039470
## iter 40 value 1.967127
## iter 60 value 1.872066
## iter 80 value 1.848833
## iter 100 value 1.732868
## iter 120 value 1.667041
## iter 140 value 1.643990
## iter 160 value 1.612288
## iter 180 value 1.607846
## iter 200 value 1.602770
## final value 1.602747
## stopped after 201 iterations
## iter 20 value 28.763949
## iter 40 value 6.715047
## iter 60 value 3.387989
## iter 80 value 2.476071
## iter 100 value 2.219852
## iter 120 value 2.062070
## iter 140 value 2.025629
## iter 160 value 1.963810
## iter 180 value 1.866328
## iter 200 value 1.751372
## final value 1.741555
## stopped after 201 iterations
## iter 20 value 25.165677
## iter 40 value 3.668156
## iter 60 value 2.556996
## iter 80 value 2.288056
## iter 100 value 2.023146
## iter 120 value 1.894138
## iter 140 value 1.865510
## iter 160 value 1.858441
## iter 180 value 1.842890
## iter 200 value 1.839585
## final value 1.839362
## stopped after 201 iterations
## validann ann L-BFGS-B i 20 summary statistics 3.4436 11.8584 2.7439 10.3669 time 0.8
##
## ________________________________________________________________________________
## *** uNeuroOne_brnn::brnn_gaussNewton ***
## 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
## 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= 5.7327 alpha= 0.0813 beta= 5.9031
## Number of parameters (weights and biases) to estimate: 6
## Nguyen-Widrow method
## Scaling factor= 1.4
## gamma= 5.7327 alpha= 0.0814 beta= 5.9029
## 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
## brnn brnn gaussNewton i 5 summary statistics 0.3523 0.1241 0.2848 0.8272 time 0
## Number of parameters (weights and biases) to estimate: 6
## Nguyen-Widrow method
## Scaling factor= 1.4
## gamma= 5.7327 alpha= 0.0813 beta= 5.9033
## 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= 0.0814 beta= 5.9027
## 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
## Number of parameters (weights and biases) to estimate: 6
## Nguyen-Widrow method
## Scaling factor= 1.4
## gamma= 5.7327 alpha= 0.0814 beta= 5.9028
## brnn brnn gaussNewton i 10 summary statistics 0.3523 0.1241 0.2848 0.8271 time 0
## 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= 5.7327 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= 0.0814 beta= 5.9029
## 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= 0.0814 beta= 5.903
## brnn brnn gaussNewton i 15 summary statistics 0.3523 0.1241 0.2848 0.827 time 0
## Number of parameters (weights and biases) to estimate: 6
## Nguyen-Widrow method
## Scaling factor= 1.4
## gamma= 5.7327 alpha= 0.0813 beta= 5.9032
## 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= 5.7327 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= 5.9032
## Number of parameters (weights and biases) to estimate: 6
## Nguyen-Widrow method
## Scaling factor= 1.4
## gamma= 5.7326 alpha= 0.0813 beta= 5.9031
## brnn brnn gaussNewton i 20 summary statistics 0.3523 0.1241 0.2848 0.8268 time 0
##
## ________________________________________________________________________________
## *** uNeuroOne_CaDENCE::cadence.fit_optim ***
## n.hidden = 2 --> 1 * NLL = -5.321044 ; penalty = 0; BIC = 28.67617 ; AICc = 14.85791 ; AIC = 9.357912
## n.hidden = 2 --> 1 * NLL = -5.321044 ; penalty = 0; BIC = 28.67617 ; AICc = 14.85791 ; AIC = 9.357912
## n.hidden = 2 --> 1 * NLL = -5.321044 ; penalty = 0; BIC = 28.67617 ; AICc = 14.85791 ; AIC = 9.357912
## n.hidden = 2 --> 1 * NLL = -5.321044 ; penalty = 0; BIC = 28.67617 ; AICc = 14.85791 ; AIC = 9.357912
## n.hidden = 2 --> 1 * NLL = -5.321044 ; penalty = 0; BIC = 28.67617 ; AICc = 14.85791 ; AIC = 9.357912
## CaDENCE cadence.fit optim i 5 summary statistics 0.2831 0.0801 0.231 0.5816 time 0.31
## n.hidden = 2 --> 1 * NLL = -5.321043 ; penalty = 0; BIC = 28.67617 ; AICc = 14.85791 ; AIC = 9.357913
## n.hidden = 2 --> 1 * NLL = -5.321044 ; penalty = 0; BIC = 28.67617 ; AICc = 14.85791 ; AIC = 9.357912
## n.hidden = 2 --> 1 * NLL = -5.321044 ; penalty = 0; BIC = 28.67617 ; AICc = 14.85791 ; AIC = 9.357912
## n.hidden = 2 --> 1 * NLL = -5.321044 ; penalty = 0; BIC = 28.67617 ; AICc = 14.85791 ; AIC = 9.357912
## n.hidden = 2 --> 1 * NLL = -5.321044 ; penalty = 0; BIC = 28.67617 ; AICc = 14.85791 ; AIC = 9.357912
## CaDENCE cadence.fit optim i 10 summary statistics 0.2831 0.0801 0.231 0.5816 time 0.25
## n.hidden = 2 --> 1 * NLL = -5.321044 ; penalty = 0; BIC = 28.67617 ; AICc = 14.85791 ; AIC = 9.357912
## n.hidden = 2 --> 1 * NLL = -5.321044 ; penalty = 0; BIC = 28.67617 ; AICc = 14.85791 ; AIC = 9.357912
## n.hidden = 2 --> 1 * NLL = -5.321044 ; penalty = 0; BIC = 28.67617 ; AICc = 14.85791 ; AIC = 9.357912
## n.hidden = 2 --> 1 * NLL = -5.321044 ; penalty = 0; BIC = 28.67617 ; AICc = 14.85791 ; AIC = 9.357912
## n.hidden = 2 --> 1 * NLL = -4.578451 ; penalty = 0; BIC = 30.16135 ; AICc = 16.3431 ; AIC = 10.8431
## CaDENCE cadence.fit optim i 15 summary statistics 0.2848 0.0811 0.2327 0.6073 time 1.11
## n.hidden = 2 --> 1 * NLL = -5.321044 ; penalty = 0; BIC = 28.67617 ; AICc = 14.85791 ; AIC = 9.357912
## n.hidden = 2 --> 1 * NLL = -5.321044 ; penalty = 0; BIC = 28.67617 ; AICc = 14.85791 ; AIC = 9.357912
## n.hidden = 2 --> 1 * NLL = -5.321044 ; penalty = 0; BIC = 28.67617 ; AICc = 14.85791 ; AIC = 9.357912
## n.hidden = 2 --> 1 * NLL = -5.321044 ; penalty = 0; BIC = 28.67617 ; AICc = 14.85791 ; AIC = 9.357912
## n.hidden = 2 --> 1 * NLL = -5.321044 ; penalty = 0; BIC = 28.67617 ; AICc = 14.85791 ; AIC = 9.357912
## CaDENCE cadence.fit optim i 20 summary statistics 0.2831 0.0801 0.231 0.5816 time 0.23
##
## ________________________________________________________________________________
## *** uNeuroOne_MachineShop::fit_none ***
## MachineShop fit none i 5 summary statistics 0.283 0.0801 0.2313 0.5675 time 0
## MachineShop fit none i 10 summary statistics 0.283 0.0801 0.2313 0.5675 time 0.02
## MachineShop fit none i 15 summary statistics 0.283 0.0801 0.2313 0.5675 time 0
## MachineShop fit none i 20 summary statistics 0.283 0.0801 0.2313 0.5675 time 0
##
## ________________________________________________________________________________
## *** uNeuroOne_minpack.lm::nlsLM_none ***
## minpack.lm nlsLM none i 5 summary statistics 0.283 0.0801 0.2313 0.5675 time 0
## minpack.lm nlsLM none i 10 summary statistics 1.272 1.6181 1.1104 2.515 time 0
## minpack.lm nlsLM none i 15 summary statistics 0.283 0.0801 0.2313 0.5675 time 0
## minpack.lm nlsLM none i 20 summary statistics 1.272 1.6181 1.1104 2.515 time 0
##
## ________________________________________________________________________________
## *** uNeuroOne_monmlp::monmlp.fit_BFGS ***
## monmlp monmlp.fit BFGS i 5 summary statistics 0.2831 0.0802 0.2312 0.5747 time 0.19
## monmlp monmlp.fit BFGS i 10 summary statistics 0.2832 0.0802 0.2312 0.575 time 0.18
## monmlp monmlp.fit BFGS i 15 summary statistics 0.2831 0.0802 0.2316 0.5602 time 0.19
## monmlp monmlp.fit BFGS i 20 summary statistics 0.2831 0.0801 0.2312 0.5681 time 0.18
##
## ________________________________________________________________________________
## *** uNeuroOne_nlsr::nlxb_none ***
## 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
## nlsr nlxb none i 5 summary statistics 0.283 0.0801 0.2313 0.5675 time 0.02
## 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
## nlsr nlxb none i 10 summary statistics 0.283 0.0801 0.2313 0.5675 time 0
## 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
## nlsr nlxb none i 15 summary statistics 0.283 0.0801 0.2313 0.5675 time 0
## 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
## nlsr nlxb none i 20 summary statistics 0.283 0.0801 0.2313 0.5675 time 0.02
##
## ________________________________________________________________________________
## *** uNeuroOne_nnet::nnet_none ***
## nnet nnet none i 5 summary statistics 0.283 0.0801 0.2313 0.5675 time 0
## nnet nnet none i 10 summary statistics 0.283 0.0801 0.2313 0.5675 time 0
## nnet nnet none i 15 summary statistics 0.283 0.0801 0.2313 0.5675 time 0
## nnet nnet none i 20 summary statistics 0.283 0.0801 0.2313 0.5675 time 0
##
## ________________________________________________________________________________
## *** uNeuroOne_qrnn::qrnn.fit_none ***
## qrnn qrnn.fit none i 5 summary statistics 0.2939 0.0864 0.2258 0.7231 time 0.08
## qrnn qrnn.fit none i 10 summary statistics 0.2939 0.0864 0.2258 0.7231 time 0.06
## qrnn qrnn.fit none i 15 summary statistics 0.2939 0.0864 0.2258 0.7231 time 0.08
## qrnn qrnn.fit none i 20 summary statistics 0.2939 0.0864 0.2258 0.7231 time 0.06
##
## ________________________________________________________________________________
## *** uNeuroOne_radiant.model::nn_none ***
## radiant.model nn none i 5 summary statistics 0.283 0.0801 0.2313 0.5674 time 0.01
## radiant.model nn none i 10 summary statistics 0.2994 0.0896 0.2481 0.6302 time 0.03
## radiant.model nn none i 15 summary statistics 0.283 0.0801 0.2313 0.5677 time 0.01
## radiant.model nn none i 20 summary statistics 0.283 0.0801 0.2313 0.568 time 0.02
##
## ________________________________________________________________________________
## *** uNeuroOne_rminer::fit_none ***
## rminer fit none i 5 summary statistics 0.283 0.0801 0.2313 0.5675 time 0
## rminer fit none i 10 summary statistics 0.283 0.0801 0.2313 0.5675 time 0
## rminer fit none i 15 summary statistics 0.283 0.0801 0.2313 0.5675 time 0
## rminer fit none i 20 summary statistics 0.283 0.0801 0.2313 0.5675 time 0.02
##
## ________________________________________________________________________________
## *** uNeuroOne_validann::ann_BFGS ***
## initial value 60.745204
## iter 20 value 3.393855
## iter 40 value 2.474854
## iter 40 value 2.474854
## iter 40 value 2.474854
## final value 2.474854
## converged
## initial value 50.969468
## iter 20 value 2.786134
## iter 40 value 2.474854
## final value 2.474854
## converged
## initial value 56.073779
## iter 20 value 2.633068
## final value 2.474854
## converged
## initial value 54.870462
## iter 20 value 3.792963
## iter 40 value 2.476419
## final value 2.474854
## converged
## initial value 60.250152
## iter 20 value 4.303535
## iter 40 value 2.986487
## iter 60 value 2.474901
## final value 2.474854
## converged
## validann ann BFGS i 5 summary statistics 0.283 0.0801 0.2313 0.5675 time 0.08
## initial value 46.048904
## iter 20 value 3.341330
## iter 40 value 2.493049
## final value 2.474854
## converged
## initial value 49.683930
## iter 20 value 2.666429
## iter 40 value 2.474978
## final value 2.474854
## converged
## initial value 44.331341
## iter 20 value 3.232810
## iter 40 value 2.474875
## final value 2.474854
## converged
## initial value 62.093910
## iter 20 value 2.474912
## final value 2.474865
## converged
## initial value 83.153694
## iter 20 value 2.885784
## iter 40 value 2.474854
## final value 2.474854
## converged
## validann ann BFGS i 10 summary statistics 0.283 0.0801 0.2313 0.5675 time 0.05
## initial value 50.729107
## iter 20 value 3.264771
## iter 40 value 2.475346
## final value 2.474854
## converged
## initial value 59.120499
## iter 20 value 6.096081
## iter 40 value 2.474854
## final value 2.474854
## converged
## initial value 52.409999
## iter 20 value 6.216408
## iter 40 value 2.477941
## final value 2.474854
## converged
## initial value 57.168506
## iter 20 value 3.571462
## iter 40 value 3.191241
## iter 60 value 2.474920
## final value 2.474854
## converged
## initial value 60.860790
## iter 20 value 2.565912
## final value 2.474854
## converged
## validann ann BFGS i 15 summary statistics 0.283 0.0801 0.2313 0.5674 time 0.05
## initial value 60.970698
## iter 20 value 4.896473
## iter 40 value 2.479882
## final value 2.474854
## converged
## initial value 69.361828
## iter 20 value 2.547456
## final value 2.474854
## converged
## initial value 54.848561
## iter 20 value 11.654548
## iter 40 value 10.824645
## iter 60 value 6.012264
## iter 80 value 2.481258
## final value 2.474854
## converged
## initial value 53.323577
## iter 20 value 4.734380
## iter 40 value 2.475431
## final value 2.474854
## converged
## initial value 69.327844
## iter 20 value 11.422574
## iter 40 value 5.567855
## iter 60 value 4.129634
## iter 80 value 2.541705
## final value 2.474854
## converged
## validann ann BFGS i 20 summary statistics 0.283 0.0801 0.2313 0.5675 time 0.12
##
## ________________________________________________________________________________
## *** uNeuroOne_validann::ann_L-BFGS-B ***
## iter 20 value 11.726836
## iter 40 value 3.730975
## iter 60 value 2.480055
## iter 80 value 2.474854
## final value 2.474854
## converged
## iter 20 value 4.980903
## iter 40 value 2.495405
## iter 60 value 2.474856
## final value 2.474854
## converged
## iter 20 value 11.260293
## iter 40 value 3.104178
## iter 60 value 2.482282
## iter 80 value 2.475408
## iter 100 value 2.474859
## final value 2.474854
## converged
## iter 20 value 11.788786
## iter 40 value 11.724430
## iter 60 value 11.722329
## final value 11.722309
## converged
## iter 20 value 11.323235
## iter 40 value 3.578071
## iter 60 value 2.608176
## iter 80 value 2.474950
## iter 100 value 2.474854
## final value 2.474854
## converged
## validann ann L-BFGS-B i 5 summary statistics 0.283 0.0801 0.2313 0.5675 time 0.14
## iter 20 value 4.166953
## iter 40 value 3.083984
## iter 60 value 2.477476
## iter 80 value 2.474914
## iter 100 value 2.474854
## final value 2.474854
## converged
## iter 20 value 2.483378
## iter 40 value 2.474854
## final value 2.474854
## converged
## iter 20 value 2.590838
## iter 40 value 2.474875
## final value 2.474854
## converged
## iter 20 value 2.890289
## iter 40 value 2.866281
## iter 60 value 2.525611
## iter 80 value 2.474854
## final value 2.474854
## converged
## iter 20 value 2.676797
## iter 40 value 2.487060
## iter 60 value 2.474854
## final value 2.474854
## converged
## validann ann L-BFGS-B i 10 summary statistics 0.283 0.0801 0.2313 0.5673 time 0.1
## iter 20 value 3.918319
## iter 40 value 3.129593
## iter 60 value 2.637684
## iter 80 value 2.485199
## iter 100 value 2.474896
## final value 2.474854
## converged
## iter 20 value 2.592707
## iter 40 value 2.476142
## iter 60 value 2.474866
## final value 2.474854
## converged
## iter 20 value 2.570339
## iter 40 value 2.476600
## iter 60 value 2.474859
## final value 2.474854
## converged
## iter 20 value 3.231290
## iter 40 value 2.491697
## iter 60 value 2.475082
## iter 80 value 2.474854
## final value 2.474854
## converged
## iter 20 value 2.507139
## iter 40 value 2.474948
## iter 60 value 2.474854
## final value 2.474854
## converged
## validann ann L-BFGS-B i 15 summary statistics 0.283 0.0801 0.2313 0.5675 time 0.08
## iter 20 value 2.571793
## iter 40 value 2.488815
## iter 60 value 2.474902
## final value 2.474854
## converged
## iter 20 value 2.744309
## iter 40 value 2.475360
## iter 60 value 2.474854
## final value 2.474854
## converged
## iter 20 value 6.505528
## iter 40 value 4.933376
## iter 60 value 3.478721
## iter 80 value 3.177305
## iter 100 value 2.526205
## iter 120 value 2.476929
## iter 140 value 2.474854
## final value 2.474854
## converged
## iter 20 value 2.558758
## iter 40 value 2.474940
## iter 60 value 2.474855
## final value 2.474854
## converged
## iter 20 value 3.267438
## iter 40 value 2.480121
## iter 60 value 2.474922
## iter 80 value 2.474854
## final value 2.474854
## converged
## validann ann L-BFGS-B i 20 summary statistics 0.283 0.0801 0.2313 0.5675 time 0.14
kable(ht(resall))
|
|
brnn::gaussNewton
|
CaDENCE::optim
|
MachineShop::none
|
minpack.lm::none
|
radiant.model::none
|
rminer::none
|
validann::BFGS
|
validann::L-BFGS-B
|
|
N1
|
1.4355
|
3.5464
|
0.3537
|
2.1450
|
0.4533
|
0.2822
|
0.5335
|
1.4449
|
|
N2
|
2.0608
|
12.5768
|
0.1251
|
4.6012
|
0.2055
|
0.0796
|
0.2846
|
2.0876
|
|
N3
|
1.2308
|
1.5083
|
0.2725
|
1.5130
|
0.3434
|
0.2198
|
0.3997
|
1.1085
|
|
N98
|
1.4612
|
0.3201
|
0.2854
|
0.2713
|
0.2812
|
0.4944
|
0.2728
|
0.8811
|
|
N99
|
12.5268
|
3.1034
|
1.8224
|
1.3253
|
1.5104
|
3.1704
|
1.3325
|
8.5046
|
|
N100
|
0.2000
|
7.2300
|
0.0700
|
0.2500
|
0.1100
|
0.2700
|
1.7200
|
1.7500
|
|
|
|
brnn::gaussNewton
|
CaDENCE::optim
|
MachineShop::none
|
minpack.lm::none
|
radiant.model::none
|
rminer::none
|
validann::BFGS
|
validann::L-BFGS-B
|
|
N1
|
0.0046
|
0.1208
|
0.0100
|
0.0150
|
0.0814
|
0.0090
|
0.0098
|
0.0820
|
|
N2
|
0.0000
|
0.0146
|
0.0001
|
0.0002
|
0.0066
|
0.0001
|
0.0001
|
0.0067
|
|
N3
|
0.0037
|
0.0507
|
0.0078
|
0.0103
|
0.0741
|
0.0070
|
0.0077
|
0.0743
|
|
N98
|
0.0037
|
0.0692
|
0.0174
|
0.0687
|
0.0084
|
0.0082
|
0.0494
|
0.0135
|
|
N99
|
0.0140
|
0.3283
|
0.0677
|
0.1698
|
0.0341
|
0.0319
|
0.1649
|
0.0557
|
|
N100
|
0.2800
|
9.8100
|
0.1100
|
0.4100
|
0.1100
|
0.2900
|
2.3500
|
2.3800
|
|
|
|
brnn::gaussNewton
|
CaDENCE::optim
|
MachineShop::none
|
minpack.lm::none
|
radiant.model::none
|
rminer::none
|
validann::BFGS
|
validann::L-BFGS-B
|
|
N1
|
3.1967
|
3.5507
|
3.5008
|
2.2994
|
2.8207
|
2.8733
|
3.2368
|
3.2858
|
|
N2
|
10.2188
|
12.6078
|
12.2558
|
5.2872
|
7.9566
|
8.2561
|
10.4769
|
10.7964
|
|
N3
|
2.5109
|
2.5927
|
2.7943
|
1.8381
|
2.2077
|
2.2501
|
2.5691
|
2.5780
|
|
N98
|
1.9659
|
4.9936
|
1.8561
|
1.8436
|
2.5650
|
1.8524
|
2.2407
|
2.7439
|
|
N99
|
7.4363
|
28.9438
|
6.3187
|
7.1469
|
9.9990
|
6.9490
|
7.8989
|
10.3669
|
|
N100
|
0.0200
|
2.3500
|
0.0300
|
0.0400
|
0.0500
|
0.0600
|
0.5900
|
0.8000
|
|
|
|
brnn::gaussNewton
|
CaDENCE::optim
|
MachineShop::none
|
minpack.lm::none
|
radiant.model::none
|
rminer::none
|
validann::BFGS
|
validann::L-BFGS-B
|
|
N1
|
0.3523
|
0.2831
|
0.2830
|
0.2830
|
0.2830
|
0.2830
|
0.2830
|
0.2830
|
|
N2
|
0.1241
|
0.0801
|
0.0801
|
0.0801
|
0.0801
|
0.0801
|
0.0801
|
0.0801
|
|
N3
|
0.2848
|
0.2310
|
0.2313
|
0.2313
|
0.2313
|
0.2313
|
0.2313
|
0.2313
|
|
N98
|
0.2848
|
0.2310
|
0.2313
|
1.1104
|
0.2313
|
0.2313
|
0.2313
|
0.2313
|
|
N99
|
0.8268
|
0.5816
|
0.5675
|
2.5150
|
0.5680
|
0.5675
|
0.5675
|
0.5675
|
|
N100
|
0.0000
|
0.2300
|
0.0000
|
0.0000
|
0.0200
|
0.0200
|
0.1200
|
0.1400
|
|