Hello,
Thanks for that. I've taken a look at the source code, and I see that
the bulk of the processing is done in C, with R acting as a wrapper.
Below is the function I think is doing the training in the network.
I'm guessing it's the standard Backpropagation with a decay term
algorithm? Can anyone confirm if that's correct?
Cheers,
Wee-Jin
------------------------------------------------------------
void
VR_dfunc(double *p, double *df, double *fp)
{
int i, j;
double sum1;
for (i = 0; i < Nweights; i++)
wts[i] = p[i];
for (j = 0; j < Nweights; j++)
Slopes[j] = 2 * Decay[j] * wts[j];
TotalError = 0.0;
for (i = 0; i < NTrain; i++) {
for (j = 0; j < Noutputs; j++)
toutputs[j] = TrainOut[i + NTrain * j];
fpass(TrainIn + i, toutputs, Weights[i], NTrain);
bpass(toutputs, Weights[i]);
}
sum1 = 0.0;
for (i = 0; i < Nweights; i++)
sum1 += Decay[i] * p[i] * p[i];
*fp = TotalError + sum1;
for (j = 0; j < Nweights; j++)
df[j] = Slopes[j];
Epoch++;
}
-----------------------------------------
On 23 Nov 2006, at 07:36, Dieter Menne wrote:
> Wee-Jin Goh <wjgoh <at> brookes.ac.uk> writes:
>
>>
>> Just to add to this, I also need to know what language is the "nnet"
>> package written in? Is it in pure R or is it a wrapper for a C
>> library.
>
> As usual, you can download the full source to find out what you
> want, but it's a
> bit hidden. Simply said, nnet (R+C) is part of package MASS is part
> of bundle
> VR, and can be downloaded as a tar.gz from
>
> http://cran.at.r-project.org/src/contrib/Descriptions/VR.html
>
> (No private flames, please, in case I should have mixed up package
> and bundle).
>
> /* nnet/nnet.c by W. N. Venables and B. D. Ripley Copyright (C)
> 1992-2002
> *
> * weights are stored in order of their destination unit.
> * the array Conn gives the source unit for the weight (0 = bias unit)
> * the array Nconn gives the number of first weight connecting to
> each unit,
> * so the weights connecting to unit i are Nconn[i] ... Nconn[i+1]
> - 1.
> *
> */
>
> #include <R.h>
> #include <R_ext/Applic.h>
>
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> PLEASE do read the posting guide http://www.R-project.org/posting-
> guide.html
> and provide commented, minimal, self-contained, reproducible code.
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