R : Copyright 2005, The R Foundation for Statistical Computing
Version 2.1.1  (2005-06-20), ISBN 3-900051-07-0

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> ### * <HEADER>
> ###
> attach(NULL, name = "CheckExEnv")
> assign(".CheckExEnv", as.environment(2), pos = length(search())) # base
> ## add some hooks to label plot pages for base and grid graphics
> setHook("plot.new", ".newplot.hook")
> setHook("persp", ".newplot.hook")
> setHook("grid.newpage", ".gridplot.hook")
> 
> assign("cleanEx",
+        function(env = .GlobalEnv) {
+ 	   rm(list = ls(envir = env, all.names = TRUE), envir = env)
+            RNGkind("default", "default")
+ 	   set.seed(1)
+    	   options(warn = 1)
+ 	   delayedAssign("T", stop("T used instead of TRUE"),
+ 		  assign.env = .CheckExEnv)
+ 	   delayedAssign("F", stop("F used instead of FALSE"),
+ 		  assign.env = .CheckExEnv)
+ 	   sch <- search()
+ 	   newitems <- sch[! sch %in% .oldSearch]
+ 	   for(item in rev(newitems))
+                eval(substitute(detach(item), list(item=item)))
+ 	   missitems <- .oldSearch[! .oldSearch %in% sch]
+ 	   if(length(missitems))
+ 	       warning("items ", paste(missitems, collapse=", "),
+ 		       " have been removed from the search path")
+        },
+        env = .CheckExEnv)
> assign("..nameEx", "__{must remake R-ex/*.R}__", env = .CheckExEnv) # for now
> assign("ptime", proc.time(), env = .CheckExEnv)
> grDevices::postscript("AMORE-Examples.ps")
> assign("par.postscript", graphics::par(no.readonly = TRUE), env = .CheckExEnv)
> options(contrasts = c(unordered = "contr.treatment", ordered = "contr.poly"))
> options(warn = 1)    
> library('AMORE')
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "newff"
> 
> ### * newff
> 
> flush(stderr()); flush(stdout())
> 
> ### Encoding: latin1
> 
> ### Name: newff
> ### Title: Create a Multilayer Feedforward Neural Network
> ### Aliases: newff
> ### Keywords: neural
> 
> ### ** Examples
> 
> #Example 1
> 
> library(AMORE)
> # P is the input vector
> P <- matrix(sample(seq(-1,1,length=1000), 1000, replace=FALSE), ncol=1) 
> # The network will try to approximate the target P^2
> target <- P^2                                   
> # We create a feedforward network, with two hidden layers.
> # The first hidden layer has three neurons and the second has two neurons.
> # The hidden layers have got Tansig activation functions and the output layer is Purelin.
> net <- newff(n.neurons=c(1,3,2,1), learning.rate.global=1e-2, momentum.global=0.5,
+         error.criterium="LMS", Stao=NA, hidden.layer="tansig", 
+         output.layer="purelin", method="ADAPTgdwm")
> net <- train(net, P, target, n.epochs=150, error.criterium="LMS", report=TRUE, show.step=5 )
Epoch: 5 LMS 0.089352660205997 
Epoch: 10 LMS 0.0893088992602254 
Epoch: 15 LMS 0.0892971907264735 
Epoch: 20 LMS 0.0892914466714754 
Epoch: 25 LMS 0.0892877870126747 
Epoch: 30 LMS 0.0892851283925426 
Epoch: 35 LMS 0.089283005703193 
Epoch: 40 LMS 0.0892811487349 
Epoch: 45 LMS 0.0892793603178988 
Epoch: 50 LMS 0.0892774659637763 
Epoch: 55 LMS 0.0892752835996779 
Epoch: 60 LMS 0.0892725970405252 
Epoch: 65 LMS 0.0892691243462514 
Epoch: 70 LMS 0.089264471589708 
Epoch: 75 LMS 0.0892580559403127 
Epoch: 80 LMS 0.0892489645965824 
Epoch: 85 LMS 0.0892356724578837 
Epoch: 90 LMS 0.0892154250816363 
Epoch: 95 LMS 0.0891827490177229 
Epoch: 100 LMS 0.089125362845532 
Epoch: 105 LMS 0.0890106829476845 
Epoch: 110 LMS 0.0887266877345177 
Epoch: 115 LMS 0.0876640427944163 
Epoch: 120 LMS 0.0755817997191597 
Epoch: 125 LMS 0.00905938314338075 
Epoch: 130 LMS 0.00513823840784522 
Epoch: 135 LMS 0.00342310653371325 
Epoch: 140 LMS 0.00252271578470017 
Epoch: 145 LMS 0.00198061886945233 
Epoch: 150 LMS 0.00162154245238247 
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  31.64 0.08 32.09 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')