On Mon, 18 Oct 2004, Samuel Kemp wrote: > I am trying to make a neural network learning a "noisy sine wave". > Suppose I generate my data like so.. > > x <- seq(-2*pi, 2*pi, length=500) > y <- sin(x) + rnorm(500, sd=sqrt(0.075)) > > I then train the neural net on the first 400 points using > > c <- nnet(as.matrix(x[1:400]),as.matrix(y[1:400]), size=3, maxit=10000, > abstol=0.075, decay=0.007) > > Inspecting the fit of the training set against the actual values using: > > pmat<- plot(y[1:400]) > lines(c$fitted.values, col="blue", lwd=2) > > It seems as though neural net is not learning the negative values. I > have tried running nnet several times, but each time I get the same > problem. I have also tried upsampling, but no joy. > > I suspect that I am not using nnet correctly. Can anyone provide any > hints/solutions?
Yes, please read the documentation, as the posting guide asks you to. Hint: it's on p. 246. Compare your example with that given there. -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595 ______________________________________________ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
